text:
doi = {10.1109/TASLP.2015.2456431},
abstract = {Authentication and tampering detection of the digital signals is one of the main applications of the digital watermarking. Recently, watermarking algorithms for digital images are developed to not only detect the image tampering, but also to recover the lost content to some extent. In this paper, a new watermarking scheme is introduced to generate digital self-embedding speech signals enjoying the self-recovery feature. For this purpose, the compressed version of the speech signal generated by a speech codec and protected against the tampering by the proper channel coding is embedded into the original speech signal. Experimental results show that the self-embedding speech signal is recoverable with proper speech quality for high tampering rates, without significant loss in the quality of the original speech signal.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1917–1925},
numpages = {9},
keywords = {digital speech self-recovery, speech authentication, digital speech watermarking, source-channel coding}
}

@article{10.1109/TASLP.2015.2449071,
author = {Tang, Duyu and Qin, Bing and Wei, Furu and Dong, Li and Liu, Ting and Zhou, Ming},
title = {A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2449071},
doi = {10.1109/TASLP.2015.2449071},
abstract = {In this paper, we propose a joint segmentation and classification framework for sentence-level sentiment classification. It is widely recognized that phrasal information is crucial for sentiment classification. However, existing sentiment classification algorithms typically split a sentence as a word sequence, which does not effectively handle the inconsistent sentiment polarity between a phrase and the words it contains, such as {"not bad," "bad"} and {"a great deal of," "great"}. We address this issue by developing a joint framework for sentence-level sentiment classification. It simultaneously generates useful segmentations and predicts sentence-level polarity based on the segmentation results. Specifically, we develop a candidate generation model to produce segmentation candidates of a sentence; a segmentation ranking model to score the usefulness of a segmentation candidate for sentiment classification; and a classification model for predicting the sentiment polarity of a segmentation. We train the joint framework directly from sentences annotated with only sentiment polarity, without using any syntactic or sentiment annotations in segmentation level. We conduct experiments for sentiment classification on two benchmark datasets: a tweet dataset and a review dataset. Experimental results show that: 1) our method performs comparably with state-of-the-art methods on both datasets; 2) joint modeling segmentation and classification outperforms pipelined baseline methods in various experimental settings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1750–1761},
numpages = {12},
keywords = {sentiment classification, natural language processing, joint segmentation and classification, artificial intelligence, sentiment analysis}
}

@article{10.1109/TASLP.2015.2462712,
author = {Wu, Ji and Li, Miao and Lee, Chin-Hui},
title = {A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2462712},
doi = {10.1109/TASLP.2015.2462712},
abstract = {In this paper, we present a probabilistic framework for goal-driven spoken dialog systems. A new dynamic stochastic state (DS-state) is then defined to characterize the goal set of a dialog state at different stages of the dialog process. Furthermore, an entropy minimization dialog management (EMDM) strategy is also proposed to combine with the DS-states to facilitate a robust and efficient solution in reaching a user's goals. A song-on-demand task, with a total of 38 117 songs and 12 attributes corresponding to each song, is used to test the performance of the proposed approach. In an ideal simulation, assuming no errors, the EMDM strategy is the most efficient goal-seeking method among all tested approaches, returning the correct song within 3.3 dialog turns on average. Furthermore, in a practical scenario, with top five candidates to handle the unavoidable automatic speech recognition (ASR) and natural language understanding (NLU) errors, the results show that only 61.7% of the dialog goals can be successfully obtained in 6.23 dialog turns on average when random questions are asked by the system, whereas if the proposed DS-states are updated with the top five candidates from the SLU output using the proposed EMDM strategy executed at every DS-state, then a 86.7% dialog success rate can be accomplished effectively within 5.17 dialog turns on average. We also demonstrate that entropy-based DM strategies are more efficient than non-entropy based DM. Moreover, using the goal set distributions in EMDM, the results are better than those without them, such as in sate-of-the-art database summary DM.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2026–2035},
numpages = {10},
keywords = {automatic speech recognition (ASR), dialog turns, spoken language understanding, dialog state modeling, probabilistic dialog representation, entropy minimization, dialog management, spoken dialog system}
}

@article{10.1109/TASLP.2015.2456430,
author = {Bokaei, Mohammad Hadi and Sameti, Hossein and Liu, Yang},
title = {Linear Discourse Segmentation of Multi-Party Meetings Based on Local and Global Information},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456430},
doi = {10.1109/TASLP.2015.2456430},
abstract = {Linear segmentation of a meeting conversation is beneficial as a stand-alone system (to organize a meeting and make it easier to access) or as a preprocessing step for many other meeting related tasks. Such segmentation can be done according to two different criteria: topic in which a meeting is segmented according to the different items in its agenda, and function in which the segmentation is done according to the meeting's different events (like discussion, monologue). In this article we concentrate on the function segmentation task and propose new unsupervised methods to segment a meeting into functionally coherent parts. The first proposed method assigns a score to each possible boundary according to its local information and then selects the best ones. The second method uses a dynamic programming approach to find the global best segmentation according to a defined cost function. Since these two methods are complementary of each other, we propose the third method as a combination of the first two ones, which takes advantage of both to improve the final segmentation. In order to evaluate our proposed methods, a subset of a standard meeting dataset (AMI) is manually annotated and used as the test set. Results show that our proposed methods perform significantly better than the previous unsupervised approach according to different evaluation metrics.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1879–1891},
numpages = {13},
keywords = {meeting function segmentation, linear discourse segmentation, dynamic programming approach, unsupervised algorithm}
}

@article{10.1109/TASLP.2015.2457612,
author = {Miao, Yajie and Zhang, Hao and Metze, Florian},
title = {Speaker Adaptive Training of Deep Neural Network Acoustic Models Using I-Vectors},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2457612},
doi = {10.1109/TASLP.2015.2457612},
abstract = {In acoustic modeling, speaker adaptive training (SAT) has been a long-standing technique for the traditional Gaussian mixture models (GMMs). Acoustic models trained with SAT become independent of training speakers and generalize better to unseen testing speakers. This paper ports the idea of SAT to deep neural networks (DNNs), and proposes a framework to perform feature-space SAT for DNNs. Using i-vectors as speaker representations, our framework learns an adaptation neural network to derive speaker-normalized features. Speaker adaptive models are obtained by fine-tuning DNNs in such a feature space. This framework can be applied to various feature types and network structures, posing a very general SAT solution. In this paper, we fully investigate how to build SAT-DNN models effectively and efficiently. First, we study the optimal configurations of SAT-DNNs for large-scale acoustic modeling tasks. Then, after presenting detailed comparisons between SAT-DNNs and the existing DNN adaptation methods, we propose to combine SAT-DNNs and model-space DNN adaptation during decoding. Finally, to accelerate learning of SAT-DNNs, a simple yet effective strategy, frame skipping, is employed to reduce the size of training data. Our experiments show that compared with a strong DNN baseline, the SAT-DNN model achieves 13.5% and 17.5% relative improvement on word error rates (WERs), without and with model-space adaptation applied respectively. Data reduction based on frame skipping results in 2\texttimes{} speed-up for SAT-DNN training, while causing negligible WER loss on the testing data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1938–1949},
numpages = {12},
keywords = {speaker adaptive training (SAT), acoustic modeling, deep neural networks (DNNs)}
}

@article{10.1109/TASLP.2015.2450491,
author = {Baby, Deepak and Virtanen, Tuomas and Gemmeke, Jort F. and Van hamme, Hugo},
title = {Coupled Dictionaries for Exemplar-Based Speech Enhancement and Automatic Speech Recognition},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2450491},
doi = {10.1109/TASLP.2015.2450491},
abstract = {Exemplar-based speech enhancement systems work by decomposing the noisy speech as a weighted sum of speech and noise exemplars stored in a dictionary and use the resulting speech and noise estimates to obtain a time-varying filter in the full-resolution frequency domain to enhance the noisy speech. To obtain the decomposition, exemplars sampled in lower dimensional spaces are preferred over the full-resolution frequency domain for their reduced computational complexity and the ability to better generalize to unseen cases. But the resulting filter may be sub-optimal as the mapping of the obtained speech and noise estimates to the full-resolution frequency domain yields a low-rank approximation. This paper proposes an efficient way to directly compute the full-resolution frequency estimates of speech and noise using coupled dictionaries: an input dictionary containing atoms from the desired exemplar space to obtain the decomposition and a coupled output dictionary containing exemplars from the full-resolution frequency domain. We also introduce modulation spectrogram features for the exemplar-based tasks using this approach. The proposed system was evaluated for various choices of input exemplars and yielded improved speech enhancement performances on the AURORA-2 and AURORA-4 databases. We further show that the proposed approach also results in improved word error rates (WERs) for the speech recognition tasks using HMM-GMM and deep-neural network (DNN) based systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1788–1799},
numpages = {12},
keywords = {modulation envelope, exemplar-based, non-negative sparse coding, noise robust automatic speech recognition}
}

@article{10.1109/TASLP.2015.2447281,
author = {Sarmiento, Auxiliadora and Dur\'{a}n-D\'{\i}az, Iv\'{a}n and Cichocki, Andrzej and Cruces, Sergio},
title = {A Contrast Function Based on Generalized Divergences for Solving the Permutation Problem in Convolved Speech Mixtures},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2447281},
doi = {10.1109/TASLP.2015.2447281},
abstract = {In this paper, we propose a method for solving the permutation problem that is inherent in the separation of convolved mixtures of speech signals in the time-frequency domain. The proposed method obtains the solution through maximization of a contrast function that exploits the similarity of the temporal envelope of the speech spectrum. For this purpose, the contrast calculation uses a global measure of similarity based on the recently developed family of generalized Alpha-Beta divergences, which depend on two tuning parameters, alpha and beta. This parameterization is exploited to best measure the similarity of the speech spectrum and to obtain solutions that are robust against noise and outliers. The ability of this contrast function to solve the permutation problem is supported by a theoretical study that shows that for a simple time-frequency speech model, the contrast value reaches its maximum when the estimated components are properly aligned. Several performance studies demonstrate that the proposed method maintains a high level of permutation correction accuracy in a wide variety of acoustic environments. Moreover, it produces better results than other state-of-the-art methods for solving permutations in highly reverberant environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1713–1726},
numpages = {14},
keywords = {speech enhancement, blind source separation (BSS), permutation problem}
}

@article{10.1109/TASLP.2015.2458580,
author = {Morfi, Veronica and Degottex, Gilles and Mouchtaris, Athanasios},
title = {Speech Analysis and Synthesis with a Computationally Efficient Adaptive Harmonic Model},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2458580},
doi = {10.1109/TASLP.2015.2458580},
abstract = {Harmonic models have to be both precise and fast in order to represent the speech signal adequately and be able to process large amount of data in a reasonable amount of time. For these purposes, the full-band adaptive harmonic model (aHM) used by the adaptive iterative refinement (AIR) algorithm has been proposed in order to accurately model the perceived characteristics of a speech signal. Even though aHM-AIR is precise, it lacks the computational efficiency that would make its use convenient for large databases. The least squares (LS) solution used in the original aHM-AIR accounts for most of the computational load. In a previous paper, we suggested a peak picking (PP) approach as a substitution to the LS solution. In order to integrate the adaptivity scheme of aHM in the PP approach, an adaptive discrete Fourier transform (aDFT), whose frequency basis can fully follow the variations of the curve, was also proposed. In this paper, we complete the previous publication by evaluating the above methods for the whole analysis process of a speech signal. Evaluations have shown an average time reduction by four times using PP and aDFT compared to the LS solution. Additionally, based on formal listening tests, when using PP and aDFT, the quality of the re-synthesis is preserved compared to the original LS-based approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1950–1962},
numpages = {13},
keywords = {voice model, peak picking (PP), fundamental frequency, speech analysis/synthesis, harmonic model}
}

@article{10.1109/TASLP.2015.2456420,
author = {Moritz, Niko and Anem\"{u}ller, J\"{o}rn and Kollmeier, Birger},
title = {An Auditory Inspired Amplitude Modulation Filter Bank for Robust Feature Extraction in Automatic Speech Recognition},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456420},
doi = {10.1109/TASLP.2015.2456420},
abstract = {The human ability to classify acoustic sounds is still unmatched compared to recent methods in machine learning. Psychoacoustic and physiological studies indicate that the auditory system of mammals decomposes audio signals into their acoustic and modulation frequency components prior to further analysis. Since it is known that most linguistic information is coded in amplitude fluctuations, mimicking temporal processing strategies of the auditory system in automatic speech recognition (ASR) promises to increase recognition accuracies. We present an amplitude modulation filter bank (AMFB) that is used as a feature extraction scheme in ASR systems. The time-frequency resolution of the employed FIR filters, i.e., bandwidth and modulation frequency settings, are adopted from a psychophysically inspired model of Dau et al. (1997) that was originally proposed to describe data from human psychoacoustics. Investigations on modulation phase indicate the need for preserving such information in amplitude modulation features. We show that the filter symmetry has an important impact on ASR performance. The proposed feature extraction scheme exhibits significant word error rate (WER) reductions using the Aurora-2, Aurora-4, and REVERB ASR tasks compared to other recent feature extraction methods, such as MFCC, FDLP, and PNCC features. Thereby, AMFB features reveal high robustness against additive noise, different transmission channel characteristics, and room reverberation. Using the Aurora-4 benchmark, for instance, an average WER of 12.33% with raw and 11.31% with bottleneck transformed features is attained, which constitutes a relative improvement of 19.6% and 29.2% over raw MFCC features, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1926–1937},
numpages = {12},
keywords = {modulation phase, bottleneck features, feature extraction, amplitude modulation filter bank}
}

@article{10.1109/TASLP.2015.2439573,
author = {Tervo, Sakari and Politis, Archontis},
title = {Direction of Arrival Estimation of Reflections from Room Impulse Responses Using a Spherical Microphone Array},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2439573},
doi = {10.1109/TASLP.2015.2439573},
abstract = {This paper studies the direction of arrival estimation of reflections in short time windows of room impulse responses measured with a spherical microphone array. Spectral-based methods, such as multiple signal classification (MUSIC) and beamforming, are commonly used in the analysis of spatial room impulse responses. However, the room acoustic reflections are highly correlated or even coherent in a single analysis window and this imposes limitations on the use of spectral-based methods. Here, we apply maximum likelihood (ML) methods, which are suitable for direction of arrival estimation of coherent reflections. These methods have been earlier developed in the linear space domain and here we present the ML methods in the context of spherical microphone array processing and room impulse responses. Experiments are conducted with simulated and real data using the em32 Eigenmike. The results show that direction estimation with ML methods is more robust against noise and less biased than MUSIC or beamforming.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1539–1551},
numpages = {13},
keywords = {room impulse response, spatial, room acoustics, direction of arrival (DOA), spherical microphone arrays}
}

@article{10.1109/TASLP.2015.2438535,
author = {Wang, Jia-Ching and Chin, Yu-Hao and Chen, Bo-Wei and Lin, Chang-Hong and Wu, Chung-Hsien},
title = {Speech Emotion Verification Using Emotion Variance Modeling and Discriminant Scale-Frequency Maps},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2438535},
doi = {10.1109/TASLP.2015.2438535},
abstract = {This paper develops an approach to speech-based emotion verification based on emotion variance modeling and discriminant scale-frequency maps. The proposed system consists of two parts---feature extraction and emotion verification. In the first part, for each sound frame, important atoms from the Gabor dictionary are selected by using the matching pursuit algorithm. The scale, frequency, and magnitude of the atoms are extracted to construct a nonuniform scale-frequency map, which supports auditory discriminability by the analysis of critical bands. Next, sparse representation is used to transform scale-frequency maps into sparse coefficients to enhance the robustness against emotion variance and achieve error-tolerance improvement. In the second part, emotion verification, two scores are calculated. A novel sparse representation verification approach based on Gaussian-modeled residual errors is proposed to generate the first score from the sparse coefficients. Such a classifier can minimize emotion variance and improve recognition accuracy. The second score is calculated by using the emotional agreement index (EAI) from the same coefficients. These two scores are combined to obtain the final detection result. Experiments on an emotional database of spoken speech were conducted and indicate that the proposed approach can achieve an average equal error rate (EER) of as low as 6.61%. A comparison among different approaches reveals that the proposed method is superior to the others and confirms its feasibility.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1552–1562},
numpages = {11},
keywords = {gaussian-modeled residual error, scale-frequency map, sparse representation, emotional speech recognition}
}

@article{10.1109/TASLP.2015.2442417,
author = {Hon, Tsz-Kin and Wang, Lin and Reiss, Joshua D. and Cavallaro, Andrea},
title = {Audio Fingerprinting for Multi-Device Self-Localization},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2442417},
doi = {10.1109/TASLP.2015.2442417},
abstract = {We investigate the self-localization problem of an ad-hoc network of randomly distributed and independent devices in an open-space environment with low reverberation but heavy noise (e.g. smartphones recording videos of an outdoor event). Assuming a sufficient number of sound sources, we estimate the distance between a pair of devices from the extreme (minimum and maximum) time difference of arrivals (TDOAs) from the sources to the pair of devices without knowing the time offset. The obtained inter-device distances are then exploited to derive the geometrical configuration of the network. In particular, we propose a robust audio fingerprinting algorithm for noisy recordings and perform landmark matching to construct a histogram of the TDOAs of multiple sources. The extreme TDOAs can be estimated from this histogram. By using audio fingerprinting features, the proposed algorithm works robustly in very noisy environments. Experiments with free-field simulation and open-space recordings prove the effectiveness of the proposed algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1623–1636},
numpages = {14},
keywords = {self-localization, ad-hoc microphone array, multi-source, time difference of arrival (TDOA) estimation, audio fingerprinting}
}

@article{10.1109/TASLP.2015.2442418,
author = {Tian, Ye and Chen, Zhe and Yin, Fuliang},
title = {Distributed Imm-Unscented Kalman Filter for Speaker Tracking in Microphone Array Networks},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2442418},
doi = {10.1109/TASLP.2015.2442418},
abstract = {In this paper, we first propose a distributed unscented Kalman filter (DUKF) to overcome the nonlinearity of measurement model in speaker tracking. Next, for the different motion dynamics of a speaker in the in-door environment, we introduce the interacting multiple model (IMM) algorithm and propose a distributed interacting multiple model-unscented Kalman filter (IMM-UKF) for estimating time-varying speaker's positions in a microphone array network. In the distributed IMM-UKF based speaker tracking method, the time difference of arrival (TDOA) of the speech signals received by a pair of microphones at each node is estimated by the generalized cross-correlation (GCC) method, then the distributed IMM-UKF is used to track a speaker whose position and speed significantly vary over time in a microphone array network. The proposed method can estimate speaker's positions globally in the network and obtain a smoothed trajectory of the speaker's movement robustly in noisy and reverberant environments, and it is scalable for speaker tracking. Simulation and real-world experiment results reveal the effectiveness of the proposed speaker tracking method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1637–1647},
numpages = {11},
keywords = {interacting multiple model, microphone array network, time difference of arrival (TDOA), speaker tracking, distributed unscented kalman filter (DUKF)}
}

@article{10.1109/TASLP.2015.2425955,
author = {Shah, Pratik and Lewis, Ian and Grant, Steven and Angrignon, Sylvain},
title = {Nonlinear Acoustic Echo Cancellation Using Voltage and Current Feedback},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2425955},
doi = {10.1109/TASLP.2015.2425955},
abstract = {Acoustic echo cancellation (AEC) is a well studied problem. The underlying assumption in most echo cancellation solutions is that the echo path following the reference signal is completely linear. However, in many handheld devices, the echo path following the reference signal is nonlinear. The reason for this nonlinearity in the echo path is the use of smaller inexpensive loudspeakers and the desire for generating high sound pressure levels. This brings about the need for a nonlinear echo canceler to maintain the required echo return loss enhancement (ERLE). Many software-based solutions have been proposed to solve this problem, but the computational complexity of these solutions is prohibitively high for practical implementation. This paper analyzes the sources of nonlinearities in smartphones and proposes a simple, elegant hardware modification to significantly reduce nonlinear echo. Thorough analysis and intensive testing results show that up to 6 dB of improvement in ERLE in a real device is possible using the proposed technique.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1589–1599},
numpages = {11},
keywords = {voltage feedback, current feedback, nonlinear echo cancellation}
}

@article{10.1109/TASLP.2015.2442411,
author = {Su, Li and Yang, Yi-Hsuan},
title = {Combining Spectral and Temporal Representations for Multipitch Estimation of Polyphonic Music},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2442411},
doi = {10.1109/TASLP.2015.2442411},
abstract = {Due to the difficulty of creating pitch-labeled training data that cover the rich diversity found in music signals, unsupervised feature-based approaches derived from signal processing and feature design remain critical for multipitch estimation (MPE) of polyphonic music. While a large number of feature representations have been proposed in the literature, an effective means of combining different domains of features for MPE is still needed. In this paper, we propose a novel approach, referred to as combined frequency and periodicity (CFP), that detects pitches according to the agreement of a harmonic series in the frequency domain and a subharmonic series in the lag (quefrency) domain. This approach nicely aggregates the complementary advantages of the two feature domains in different frequency ranges, and improves the robustness of the pitch detection function to the interference of the overtones of simultaneous pitches. We report a comprehensive evaluation that compares CFP against three state-of-the-art approaches using three MPE datasets and four symphonies. The evaluation is characteristic of the coverage and complexity of music (in terms of instrument type and degree of polyphony). In addition, we also evaluate the performance of the MPE approaches when a number of audio degradations are applied. Results show that the proposed unsupervised method performs consistently well across the types of Western polyphonic music considered, and is robust to audio degradations such as high-pass filtering and MP3 compression.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1600–1612},
numpages = {13},
keywords = {unsupervised approach, generalized cepstrum, multipitch estimation, automatic music transcription}
}

@article{10.1109/TASLP.2015.2439577,
author = {He, Jianjun and Gan, Woon-Seng and Tan, Ee-Leng},
title = {Time-Shifting Based Primary-Ambient Extraction for Spatial Audio Reproduction},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2439577},
doi = {10.1109/TASLP.2015.2439577},
abstract = {One of the key issues in spatial audio analysis and reproduction is to decompose a signal into primary and ambient components based on their directional and diffuse spatial features, respectively. Existing approaches employed in primary-ambient extraction (PAE), such as principal component analysis (PCA), are mainly based on a basic stereo signal model. The performance of these PAE approaches has not been well studied for the input signals that do not satisfy all the assumptions of the stereo signal model. In practice, one such case commonly encountered is that the primary components of the stereo signal are partially correlated at zero lag, referred to as the primary-complex case. In this paper, we take PCA as a representative of existing PAE approaches and investigate the performance degradation of PAE with respect to the correlation of the primary components in the primary-complex case. A time-shifting technique is proposed in PAE to alleviate the performance degradation due to the low correlation of the primary components in such stereo signals. This technique involves time-shifting the input signal according to the estimated inter-channel time difference of the primary component prior to the signal decomposition using conventional PAE approaches. To avoid the switching artifacts caused by the varied time-shifting in successive time frames, overlapped output mapping is suggested. Based on the results from our experiments, PAE approaches with the proposed time-shifting technique are found to be superior to the conventional PAE approaches in terms of extraction accuracy and spatial accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1576–1588},
numpages = {13},
keywords = {spatial audio, spatial cues, primary-ambient extraction (PAE), principal component analysis (PCA)}
}

@article{10.1109/TASLP.2015.2442415,
author = {Fujioka, Toyota and Nagata, Yoshifumi and Abe, Masato},
title = {High-Precision Harmonic Distortion Level Measurement of a Loudspeaker Using Adaptive Filters in a Noisy Environment},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2442415},
doi = {10.1109/TASLP.2015.2442415},
abstract = {The harmonic distortion level (HDL), an important criterion used to evaluate loudspeaker performance, can usually be measured using spectral analysis. However, spectral analysis entails great computational complexity. Furthermore, long measurement times are necessary to measure HDL accurately in a noisy environment. Therefore, we proposed the HDL measurement technique for a loudspeaker using an adaptive filter. This measurement technique can measure HDL as accurately as spectral analysis using fast Fourier transform (FFT). Furthermore, its computational complexity is much less than that of spectral analysis. Nevertheless, this measurement technique requires a long measurement time to assess the convergence of filter coefficients. This paper presents a description of the new HDL measurement technique of a loudspeaker using plural adaptive filters. The proposed measurement technique can measure HDL more accurately than spectral analysis in a noisy environment. We evaluated the performance of the proposed measurement technique using computer simulations. Results based on computer simulations show that the proposed measurement technique is more effective than spectral analysis in an acoustic environment with background noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1613–1622},
numpages = {10},
keywords = {harmonic distortion, loudspeaker, adaptive filter, distortion level measurement}
}

@article{10.1109/TASLP.2015.2444664,
author = {Ruhland, Marco and Bitzer, Joerg and Brandt, Matthias and Goetze, Stefan},
title = {Reduction of Gaussian, Supergaussian, and Impulsive Noise by Interpolation of the Binary Mask Residual},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2444664},
doi = {10.1109/TASLP.2015.2444664},
abstract = {In this paper, we present a new approach for noise reduction. A binary time-frequency (T-F) masking threshold criterion is proposed and analyzed with respect to the average spectra of music and noise disturbances. Modified autoregressive (AR) detection and AR interpolation are then applied to the residual signal of the binary masking process. The proposed method is able to reduce supergaussian and impulsive noise while ensuring preservation of the desired signal, which is crucial for professional high-quality audio restoration, and it is also suitable for Gaussian noise to a certain extent. The approach is compared to a state-of-the-art restoration algorithm by means of the objective measures signal-to-noise ratio (SNR) improvement and perceptual quality, and by subjective listening tests. The objective results as well as the listening tests show that the proposed algorithm is especially suited for supergaussian, grainy-sounding noise types, e.g., optical soundtrack noise of celluloid movie footage, or rain noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1680–1691},
numpages = {12},
keywords = {optical soundtrack noise, time-frequency masking, interpolation, noise reduction}
}

@article{10.1109/TASLP.2015.2443977,
author = {Vilkamo, Juha and Delikaris-Manias, Symeon},
title = {Perceptual Reproduction of Spatial Sound Using Loudspeaker-Signal-Domain Parametrization},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2443977},
doi = {10.1109/TASLP.2015.2443977},
abstract = {Adaptive perceptual spatial sound reproduction techniques that employ a parametric model describing the properties of the sound field can reproduce spatial sound with high perceptual accuracy when compared to linear techniques. On the other hand, applying a sound-field model to control the reproduced sound may compromise the perceived quality of individual channels in cases where the model does not match the sound field. An alternative parametrization is proposed that estimates directly the perceptually relevant parameters for the target loudspeaker signals without modeling the sound field. At the synthesis stage, the loudspeaker signals with the target parametric properties are generated from the microphone signals with regularized leastsquares mixing and decorrelation. It is shown through listening experiments that the proposed method provides on average the overall perceived spatial sound reproduction quality of a state-of the-art parametric spatial sound reproduction technique, while solving the past shortcomings related to the perceived quality of the individual channels.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1660–1669},
numpages = {10},
keywords = {spatial audio reproduction, least-squares mixing, sound-field parametrization, microphone array signal processing}
}

@article{10.1109/TASLP.2015.2442757,
author = {Li, Na and Mak, Man-Wai},
title = {SNR-Invariant PLDA Modeling in Nonparametric Subspace for Robust Speaker Verification},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2442757},
doi = {10.1109/TASLP.2015.2442757},
abstract = {While i-vector/PLDA framework has achieved great success, its performance still degrades dramatically under noisy conditions. To compensate for the variability of i-vectors caused by different levels of background noise, this paper proposes an SNR-invariant PLDA framework for robust speaker verification. First, nonparametric feature analysis (NFA) is employed to suppress intra-speaker variation and emphasize the discriminative information inherited in the boundaries between speakers in the i-vector space. Then, in the NFA-projected subspace, SNR-invariant PLDA is applied to separate the SNR-specific information from speaker-specific information using an identity factor and an SNR factor. Accordingly, a projected i-vector in the NFA subspace can be represented as a linear combination of three components: speaker, SNR, and channel. During verification, the variability due to SNR and channels are integrated out when computing the marginal likelihood ratio. Experiments based on NIST 2012 SRE show that the proposed framework achieves superior performance when compared with the conventional PLDA and SNR-dependent mixture of PLDA.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1648–1659},
numpages = {12},
keywords = {probabilistic linear discriminant analysis (PLDA), speaker verification, i-vector, nonparametric feature analysis, SNR-invariant}
}

@article{10.1109/TASLP.2015.2444654,
author = {Dorfan, Yuval and Gannot, Sharon},
title = {Tree-Based Recursive Expectation-Maximization Algorithm for Localization of Acoustic Sources},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2444654},
doi = {10.1109/TASLP.2015.2444654},
abstract = {The problem of distributed localization for ad hoc wireless acoustic sensor networks (WASNs) is addressed in this paper. WASNs are characterized by low computational resources in each node and by limited connectivity between the nodes. Novel bi-directional tree-based distributed estimation--maximization (DEM) algorithms are proposed to circumvent these inherent limitations. We show that the proposed algorithms are capable of localizing static acoustic sources in reverberant enclosures without a priori information on the number of sources. Unlike serial estimation procedures (like ring-based algorithms), the new algorithms enable simultaneous computations in the nodes and exhibit greater robustness to communication failures. Specifically, the recursive distributed EM (RDEM) variant is better suited to online applications due to its recursive nature. Furthermore, the RDEM outperforms the other proposed variants in terms of convergence speed and simplicity. Performance is demonstrated by an extensive experimental study consisting of both simulated and actual environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1692–1703},
numpages = {12},
keywords = {speaker localization, recursive expectation-maximization (REM), wireless acoustic sensor networks (WASNs), bi-directional tree topologies, distributed signal processing}
}

@article{10.1109/TASLP.2015.2439040,
author = {Canclini, A. and Bestagini, P. and Antonacci, F. and Compagnoni, M. and Sarti, A. and Tubaro, S.},
title = {A Robust and Low-Complexity Source Localization Algorithm for Asynchronous Distributed Microphone Networks},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2439040},
doi = {10.1109/TASLP.2015.2439040},
abstract = {In this paper, we propose a robust and low-complexity acoustic source localization technique based on time differences of arrival (TDOA), which addresses the scenario of distributed sensor networks in 3D environments. Network nodes are assumed to be unsynchronized, i.e., TDOAs between microphones belonging to different nodes are not available. We begin with showing how to select feasible TDOAs for each sensor node, exploiting both geometrical considerations and a characterization of the overall generalized cross correlation (GCC) shape. We then show how to localize sources in the space-range reference frame, where TDOA measurements have a clear geometrical interpretation that can be fruitfully used in the scenario of unsynchronized sensors. In this framework, in fact, the source corresponds to the apex of a hypercone passing through points described by the sole microphone positions and TDOA measurements. The localization problem is therefore approached as a hypercone fitting problem. Finally, in order to improve the robustness of the estimate, we include an outlier detection procedure based on the evaluation of the hypercone fitting residuals. A refinement of source location estimate is then performed ignoring the contributions coming from outlier measurements. A set of simulations shows the performance of individual blocks of the system, with particular focus on the effect of TDOA selection on source localization and refinement steps. Experiments on real data validate the localization algorithm in an everyday scenario, proving that good accuracy can be obtained while saving computational cost in comparison with state-of-the-art techniques.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1563–1575},
numpages = {13},
keywords = {space-range reference frame, distributed acosutic sensor networks, acoustic source localization, time difference of arrival (TDOA) selection}
}

@article{10.1109/TASLP.2015.2444659,
author = {Weng, Chao and Yu, Dong and Seltzer, Michael L. and Droppo, Jasha},
title = {Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition},
year = {2015},
issue_date = {Octember 2015},
publisher = {IEEE Press},
volume = {23},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2444659},
doi = {10.1109/TASLP.2015.2444659},
abstract = {We investigate techniques based on deep neural networks (DNNs) for attacking the single-channel multi-talker speech recognition problem. Our proposed approach contains five key ingredients: a multi-style training strategy on artificially mixed speech data, a separate DNN to estimate senone posterior probabilities of the louder and softer speakers at each frame, a weighted finite-state transducer (WFST)-based two-talker decoder to jointly estimate and correlate the speaker and speech, a speaker switching penalty estimated from the energy pattern change in the mixed-speech, and a confidence based system combination strategy. Experiments on the 2006 speech separation and recognition challenge task demonstrate that our proposed DNN-based system has remarkable noise robustness to the interference of a competing speaker. The best setup of our proposed systems achieves an average word error rate (WER) of 18.8% across different SNRs and outperforms the state-of-the-art IBM superhuman system by 2.8% absolute with fewer assumptions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1670–1679},
numpages = {10},
keywords = {joint decoding, deep neural network (DNN), noise robustness, multi-talker automatic speech recognition (ASR), weighted finite-state transducer (WFST), single-channel}
}

@article{10.1109/TASLP.2015.2438543,
author = {Lee, Lin-shan and Glass, James and Lee, Hung-yi and Chan, Chun-an},
title = {Spoken Content Retrieval: Beyond Cascading Speech Recognition with Text Retrieval},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2438543},
doi = {10.1109/TASLP.2015.2438543},
abstract = {Spoken content retrieval refers to directly indexing and retrieving spoken content based on the audio rather than text descriptions. This potentially eliminates the requirement of producing text descriptions for multimedia content for indexing and retrieval purposes, and is able to precisely locate the exact time the desired information appears in the multimedia. Spoken content retrieval has been very successfully achieved with the basic approach of cascading automatic speech recognition (ASR) with text information retrieval: after the spoken content is transcribed into text or lattice format, a text retrieval engine searches over the ASR output to find desired information. This framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1389–1420},
numpages = {32},
keywords = {semantic retrieval, key term extraction, graph-based random walk, unsupervised acoustic pattern discovery, interactive retrieval, spoken content retrieval, summarization, pseudo-relevance feedback, query expansion, joint optimization, spoken term detection, query by example}
}

@article{10.1109/TASLP.2015.2439038,
author = {Mowlaee, Pejman and Kulmer, Josef},
title = {Harmonic Phase Estimation in Single-Channel Speech Enhancement Using Phase Decomposition and SNR Information},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2439038},
doi = {10.1109/TASLP.2015.2439038},
abstract = {In conventional single-channel speech enhancement, typically the noisy spectral amplitude is modified while the noisy phase is used to reconstruct the enhanced signal. Several recent attempts have shown the effectiveness of utilizing an improved spectral phase for phase-aware speech enhancement and consequently its positive impact on the perceived speech quality. In this paper, we present a harmonic phase estimation method relying on fundamental frequency and signal-to-noise ratio (SNR) information estimated from noisy speech. The proposed method relies on SNR-based time-frequency smoothing of the unwrapped phase obtained from the decomposition of the noisy phase. To incorporate the uncertainty in the estimated phase due to unreliable voicing decision and SNR estimate, we propose a binary hypothesis test assuming speech-present and speech-absent classes representing high and low SNRs. The effectiveness of the proposed phase estimation method is evaluated for both phase-only enhancement of noisy speech and in combination with an amplitude-only enhancement scheme. We show that by enhancing the noisy phase both perceived speech quality as well as speech intelligibility are improved as predicted by the instrumental metrics and justified by subjective listening tests.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1521–1532},
numpages = {12},
keywords = {noise reduction, harmonic model, phase decomposition, phase estimation, von Mises distribution}
}

@article{10.1109/TASLP.2015.2434272,
author = {He, Jianjun and Gan, Woon-Seng and Tan, Ee-Leng},
title = {Primary-Ambient Extraction Using Ambient Spectrum Estimation for Immersive Spatial Audio Reproduction},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2434272},
doi = {10.1109/TASLP.2015.2434272},
abstract = {The diversity of today's playback systems requires a flexible, efficient, and immersive reproduction of sound scenes in digital media. Spatial audio reproduction based on primary-ambient extraction (PAE) fulfills this objective, where accurate extraction of primary and ambient components from sound mixtures in channel-based audio is crucial. Severe extraction error was found in existing PAE approaches when dealing with sound mixtures that contain a relatively strong ambient component, a commonly encountered case in the sound scenes of digital media. In this paper, we propose a novel ambient spectrum estimation (ASE) framework to improve the performance of PAE. The ASE framework exploits the equal magnitude of the uncorrelated ambient components in two channels of a stereo signal, and reformulates the PAE problem into the problem of estimating either ambient phase or magnitude. In particular, we take advantage of the sparse characteristic of the primary components to derive sparse solutions for ASE based PAE, together with an approximate solution that can significantly reduce the computational cost. Our objective and subjective experimental results demonstrate that the proposed ASE approaches significantly outperform existing approaches, especially when the ambient component is relatively strong.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1431–1444},
numpages = {14},
keywords = {sparsity, spatial audio, ambient spectrum estimation (ASE), computational efficiency, primary-ambient extraction (PAE)}
}

@article{10.1109/TASLP.2015.2436345,
author = {Chien, Yu-Ren and Wang, Hsin-Min and Jeng, Shyh-Kang},
title = {An Acoustic-Phonetic Model of F0 Likelihood for Vocal Melody Extraction},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2436345},
doi = {10.1109/TASLP.2015.2436345},
abstract = {This paper presents a novel approach to extraction of vocal melodies from accompanied singing recordings. Central to our approach is a model of vocal fundamental frequency (F0) likelihood that integrates acoustic-phonetic knowledge and real-world data. This model consists of a timbral fitness score and a loudness measure of each F0 candidate. Timbral fitness is measured for the partial amplitudes of an F0 candidate, with respect to a small set of vocal timbre examples. This F0-specific measurement of timbral fitness depends on an acoustic-phonetic F0 modification of each timbre example. In the loudness part of the likelihood model, sinusoids are detected, tracked, and pruned to give loudness values that minimize interference from the accompaniment. A final F0 estimate is determined by a prior model of F0 sequence in addition to the likelihood model. Melody extraction is completed by detecting voiced time positions according to the singing voice loudness variations given by the estimated F0 sequence. The numerical parameters involved in our approach were optimized on three development sets from different sources before the system was evaluated on ten test sets separate from these development sets. Controlled experiments show that use of the timbral fitness score accounts for a 13% difference in overall accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1457–1468},
numpages = {12},
keywords = {acoustic phonetics, singing voice, F0 modification, melody extraction, vocal timbre examples, F0 likelihood, F0 estimation}
}

@article{10.1109/TASLP.2015.2438547,
author = {De Sena, Enzo and Hacιhabibo\u{g}lu, H\"{u}seyin and Cvetkovi\'{c}, Zoran and Smith, Julius O.},
title = {Efficient Synthesis of Room Acoustics via Scattering Delay Networks},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2438547},
doi = {10.1109/TASLP.2015.2438547},
abstract = {An acoustic reverberator consisting of a network of delay lines connected via scattering junctions is proposed. All parameters of the reverberator are derived from physical properties of the enclosure it simulates. It allows for simulation of unequal and frequency-dependent wall absorption, as well as directional sources and microphones. The reverberator renders the first-order reflections exactly, while making progressively coarser approximations of higher-order reflections. The rate of energy decay is close to that obtained with the image method (IM) and consistent with the predictions of Sabine and Eyring equations. The time evolution of the normalized echo density, which was previously shown to be correlated with the perceived texture of reverberation, is also close to that of the IM. However, its computational complexity is one to two orders of magnitude lower, comparable to the computational complexity of a feedback delay network and its memory requirements are negligible.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1478–1492},
numpages = {15},
keywords = {room acoustics, echo density, acoustic simulation, reverberation time, digital waveguide network}
}

@article{10.1109/TASLP.2015.2438542,
author = {Wang, Lin and Gerkmann, Timo and DoclO, Simon},
title = {Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2438542},
doi = {10.1109/TASLP.2015.2438542},
abstract = {In this paper, a multi-microphone noise reduction system based on the generalized sidelobe canceller (GSC) structure is investigated. The system consists of a fixed beamformer providing an enhanced speech reference, a blocking matrix providing a noise reference by suppressing the target speech, and a single-channel spectral post-filter. The spectral post-filter requires the power spectral density (PSD) of the residual noise in the speech reference, which can in principle be estimated from the PSD of the noise reference. However, due to speech leakage in the noise reference, the noise PSD is overestimated, leading to target speech distortion. To minimize the influence of the speech leakage, a maximum noise-to-speech ratio (MaxNSR) blocking matrix is proposed, which maximizes the ratio between the noise and the speech leakage in the noise reference. The proposed blocking matrix can be computed from the generalized eigenvalue decomposition of the correlation matrix of the microphone signals and the noise coherence matrix, which is assumed to be time-invariant. Experimental results in both stationary and nonstationary diffuse noise fields show that the proposed algorithm outperforms existing blocking matrices in terms of target speech blocking ability, noise estimation and noise reduction performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1493–1508},
numpages = {16},
keywords = {blocking matrix, diffuse noise, speech enhancement, noise power spectral density (PSD) estimation, microphone array}
}

@article{10.1109/TASLP.2015.2434213,
author = {Jiao, Yishan and Berisha, Visar and Tu, Ming and Liss, Julie},
title = {Convex Weighting Criteria for Speaking Rate Estimation},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2434213},
doi = {10.1109/TASLP.2015.2434213},
abstract = {Speaking rate estimation directly from the speech waveform is a long-standing problem in speech signal processing. In this paper, we pose the speaking rate estimation problem as that of estimating a temporal density function whose integral over a given interval yields the speaking rate within that interval. In contrast to many existing methods, we avoid the more difficult task of detecting individual phonemes within the speech signal and we avoid heuristics such as thresholding the temporal envelope to estimate the number of vowels. Rather, the proposed method aims to learn an optimal weighting function that can be directly applied to time-frequency features in a speech signal to yield a temporal density function. We propose two convex cost functions for learning the weighting functions and an adaptation strategy to customize the approach to a particular speaker using minimal training. The algorithms are evaluated on the TIMIT corpus, on a dysarthric speech corpus, and on the ICSI Switchboard spontaneous speech corpus. Results show that the proposed methods outperform three competing methods on both healthy and dysarthric speech. In addition, for spontaneous speech rate estimation, the result show a high correlation between the estimated speaking rate and ground truth values.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1421–1430},
numpages = {10},
keywords = {speaker adaptation, vowel density function, dysarthria, convex optimization, speaking rate estimation}
}

@article{10.1109/TASLP.2015.2438549,
author = {Juki\'{c}, Ante and van Waterschoot, Toon and Gerkmann, Timo and Doclo, Simon},
title = {Multi-Channel Linear Prediction-Based Speech Dereverberation with Sparse Priors},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2438549},
doi = {10.1109/TASLP.2015.2438549},
abstract = {The quality of speech signals recorded in an enclosure can be severely degraded by room reverberation. In this paper, we focus on a class of blind batch methods for speech dereverberation in a noiseless scenario with a single source, which are based on multi-channel linear prediction in the short-time Fourier transform domain. Dereverberation is performed by maximum-likelihood estimation of the model parameters that are subsequently used to recover the desired speech signal. Contrary to the conventional method, we propose to model the desired speech signal using a general sparse prior that can be represented in a convex form as a maximization over scaled complex Gaussian distributions. The proposed model can be interpreted as a generalization of the commonly used time-varying Gaussian model. Furthermore, we reformulate both the conventional and the proposed method as an optimization problem with an ℓp-norm cost function, emphasizing the role of sparsity in the considered speech dereverberation methods. Experimental evaluation in different acoustic scenarios show that the proposed approach results in an improved performance compared to the conventional approach in terms of instrumental measures for speech quality.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1509–1520},
numpages = {12},
keywords = {speech dereverberation, speech enhancement, multi-channel linear prediction, sparse priors}
}

@article{10.1109/TASLP.2015.2438544,
author = {Cui, Xiaodong and Goel, Vaibhava and Kingsbury, Brian},
title = {Data Augmentation for Deep Neural Network Acoustic Modeling},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2438544},
doi = {10.1109/TASLP.2015.2438544},
abstract = {This paper investigates data augmentation for deep neural network acoustic modeling based on label-preserving transformations to deal with data sparsity. Two data augmentation approaches, vocal tract length perturbation (VTLP) and stochastic feature mapping (SFM), are investigated for both deep neural networks (DNNs) and convolutional neural networks (CNNs). The approaches are focused on increasing speaker and speech variations of the limited training data such that the acoustic models trained with the augmented data are more robust to such variations. In addition, a two-stage data augmentation scheme based on a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Experiments are conducted on Assamese and Haitian Creole, two development languages of the IARPA Babel program, and improved performance on automatic speech recognition (ASR) and keyword search (KWS) is reported.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1469–1477},
numpages = {9},
keywords = {automatic speech recognition, data augmentation, stochastic feature mapping, deep neural networks, keyword search}
}

@article{10.1109/TASLP.2015.2436214,
author = {Shen, Qing and Liu, Wei and Cui, Wei and Wu, Siliang and Zhang, Yimin D. and Amin, Moeness G.},
title = {Low-Complexity Direction-of-Arrival Estimation Based on Wideband Co-Prime Arrays},
year = {2015},
issue_date = {September 2015},
publisher = {IEEE Press},
volume = {23},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2436214},
doi = {10.1109/TASLP.2015.2436214},
abstract = {A class of low-complexity compressive sensing-based direction-of-arrival (DOA) estimation methods for wideband co-prime arrays is proposed. It is based on a recently proposed narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and then using a sparse signal recovery method to obtain the estimation result. As there are a large number of redundant entries in both the auto-correlation and cross-correlation matrices of the two sub-arrays, they can be combined together to form a model with a significantly reduced dimension, thereby leading to a solution with much lower computational complexity without sacrificing performance. A further reduction in complexity is achieved by removing noise power estimation from the formulation. Then, the two proposed low-complexity methods are extended to the wideband realm utilizing a group sparsity based signal reconstruction method. A particular advantage of group sparsity is that it allows a much larger unit inter-element spacing than the standard co-prime array and therefore leads to further improved performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1445–1453},
numpages = {9},
keywords = {co-prime, direction-of-arrival (DOA) estimation, wideband, microphone arrays, sparsity}
}

@article{10.1109/TASLP.2015.2432578,
author = {Chen, Kuan-Yu and Liu, Shih-Hung and Chen, Berlin and Wang, Hsin-Min and Jan, Ea-Ee and Hsu, Wen-Lian and Chen, Hsin-Hsi},
title = {Extractive Broadcast News Summarization Leveraging Recurrent Neural Network Language Modeling Techniques},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2432578},
doi = {10.1109/TASLP.2015.2432578},
abstract = {Extractive text or speech summarization manages to select a set of salient sentences from an original document and concatenate them to form a summary, enabling users to better browse through and understand the content of the document. A recent stream of research on extractive summarization is to employ the language modeling (LM) approach for important sentence selection, which has proven to be effective for performing speech summarization in an unsupervised fashion. However, one of the major challenges facing the LM approach is how to formulate the sentence models and accurately estimate their parameters for each sentence in the document to be summarized. In view of this, our work in this paper explores a novel use of recurrent neural network language modeling (RNNLM) framework for extractive broadcast news summarization. On top of such a framework, the deduced sentence models are able to render not only word usage cues but also long-span structural information of word co-occurrence relationships within broadcast news documents, getting around the need for the strict bag-of-words assumption. Furthermore, different model complexities and combinations are extensively analyzed and compared. Experimental results demonstrate the performance merits of our summarization methods when compared to several well-studied state-of-the-art unsupervised methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1322–1334},
numpages = {13},
keywords = {language modeling, long-span structural information, recurrent neural network, speech summarization}
}

@article{10.1109/TASLP.2015.2427522,
author = {Momeni, Hajar and Abutalebi, Hamid Reza and Tadaion, Aliakbar},
title = {Joint Detection and Estimation of Speech Spectral Amplitude Using Noncontinuous Gain Functions},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2427522},
doi = {10.1109/TASLP.2015.2427522},
abstract = {This paper addresses the joint detection and estimation approach for single-channel speech enhancement. In this approach, a detector decides on speech presence in each time-frequency unit and an estimator estimates the corresponding speech spectral amplitude. We utilize the concept of binary/continuous gain functions to study and extend the process of joint detection and estimation. The binary gains (BGs) have already shown an inferior performance compared to the continuous gains (CGs). In this paper, we propose a simultaneous detection and estimation (SDE) method where the detector structure is derived by the knowledge of the estimator. The proposed SDE method is a combination of Bayesian and Neyman-Pearson approaches and is expressed as a noncontinuous gain (NCG). It is expected that employing a superior detector, the proposed NCG improves the quality of the output speech. We concentrate on the derivation of the detector so that it minimizes the error caused by missed detection and/or wrong estimation of speech coefficients at a controlled level of falsely detecting high-energy noise as speech. Furthermore, an independent detection and estimation technique is proposed where the detector and the estimator are extracted in an independent manner. Simulation results demonstrate that the proposed SDE method minimizes the speech distortion at a controlled level of noise reduction. It is also shown that the performance of the proposed NCG is better than the CG and than the existing BGs in both noise reduction and speech distortion aspects.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1249–1258},
numpages = {10},
keywords = {joint detection and estimation, speech enhancement, spectral amplitude estimation, speech detection}
}

@article{10.1109/TASLP.2015.2430818,
author = {Fallahpour, Mehdi and Meg\'{\i}as, David},
title = {Audio Watermarking Based on Fibonacci Numbers},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2430818},
doi = {10.1109/TASLP.2015.2430818},
abstract = {This paper presents a novel high-capacity audio watermarking system to embed data and extract them in a bit-exact manner by changing some of the magnitudes of the FFT spectrum. The key idea is to divide the FFT spectrum into short frames and change the magnitude of the selected FFT samples using Fibonacci numbers. Taking advantage of Fibonacci numbers, it is possible to change the frequency samples adaptively. In fact, the suggested technique guarantees and proves, mathematically, that the maximum change is less than 61% of the related FFT sample and the average error for each sample is 25%. Using the closest Fibonacci number to FFT magnitudes results in a robust and transparent technique. On top of very remarkable capacity, transparency and robustness, this scheme provides two parameters which facilitate the regulation of these properties. The experimental results show that the method has a high capacity (700 bps to 3 kbps), without significant perceptual distortion (ODG is about 1) and provides robustness against common audio signal processing such as echo, added noise, filtering, and MPEG compression (MP3). In addition to the experimental results, the fidelity of suggested system is proved mathematically.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1273–1282},
numpages = {10},
keywords = {Fibonacci numbers, multimedia security, audio watermarking, golden ratio}
}

@article{10.1109/TASLP.2015.2428632,
author = {Chien, Jen-Tzung},
title = {Hierarchical Pitman-Yor-Dirichlet Language Model},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2428632},
doi = {10.1109/TASLP.2015.2428632},
abstract = {Probabilistic models are often viewed as insufficiently expressive because of strong limitation and assumption on the probabilistic distribution and the fixed model complexity. Bayesian nonparametric learning pursues an expressive probabilistic representation based on the nonparametric prior and posterior distributions with less assumption-laden approach to inference. This paper presents a hierarchical Pitman-Yor-Dirichlet (HPYD) process as the nonparametric priors to infer the predictive probabilities of the smoothed n-grams with the integrated topic information. A metaphor of hierarchical Chinese restaurant process is proposed to infer the HPYD language model (HPYD-LM) via Gibbs sampling. This process is equivalent to implement the hierarchical Dirichlet process-latent Dirichlet allocation (HDP-LDA) with the twisted hierarchical Pitman-Yor LM (HPY-LM) as base measures. Accordingly, we produce the power-law distributions and extract the semantic topics to reflect the properties of natural language in the estimated HPYD-LM. The superiority of HPYD-LM to HPY-LM and other language models is demonstrated by the experiments on model perplexity and speech recognition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1259–1272},
numpages = {14},
keywords = {unsupervised learning, language model, topic model, Bayesian nonparametrics, speech recognition}
}

@article{10.1109/TASLP.2015.2431854,
author = {Morchid, Mohamed and Bouallegue, Mohamed and Dufour, Richard and Linar\`{e}s, Georges and Matrouf, Driss and De Mori, Renato},
title = {Compact Multiview Representation of Documents Based on the Total Variability Space},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2431854},
doi = {10.1109/TASLP.2015.2431854},
abstract = {Mapping text documents in an LDA-based topic-space is a classical way to extract high-level representation of text documents. Unfortunately, LDA is highly sensitive to hyper-parameters related to the number of classes, or word and topic distribution, and there is no systematic way to pre-estimate optimal configurations. Moreover, various hyper-parameter configurations offer complementary views on the document. In this paper, we propose a method based on a two-step process that, first, expands the representation space by using a set of topic spaces and, second, compacts the representation space by removing poorly relevant dimensions. These two steps are based respectively on multi-view LDA-based representation spaces and factor-analysis models. This model provides a view-independent representation of documents while extracting complementary information from a massive multi-view representation. Experiments are conducted on the DECODA conversation corpus and the Reuters-21578 textual dataset. Results show the efficiency of the proposed multiview compact representation paradigm. The proposed categorization system reaches an accuracy of 86.5% with automatic transcriptions of conversations from DECODA corpus and a Macro-F1 of 80% during a classification task of the well-known Reuters-21578 corpus, with a significant gain compared to the baseline (best single topic space configuration), as well as methods and document representations previously studied.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1295–1308},
numpages = {14},
keywords = {classification, latent Dirichlet allocation, C-vector, factor analysis}
}

@article{10.5555/2876444.2876454,
author = {Wang, Xun and Yoshida, Yasuhisa and Hirao, Tsutomu and Sudoh, Katsuhito and Nagata, Masaaki},
title = {Summarization Based on Task-Oriented Discourse Parsing},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
abstract = {Previous research demonstrates that discourse relations can help generate high-quality summaries. Existing studies usually adopt existing discourse parsers directly with no modifications, hence cannot take full advantage of discourse parsing. This paper describes a new single document summarization system. In contrast to previous work, we train a discourse parser specially for summarization by using summaries. The training data are dynamically changed during the training phase to enable the parser to grab the text units that are important for summaries. A special tree-based summary extraction algorithm is designed to work with the new parser. The proposed system enables us to combine discourse parsing and summarization in a unified scheme. Experiments on both the RST-DT and DUC2001 datasets show the effectiveness of the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1358–1367},
numpages = {10},
keywords = {discourse parsing, summarization, discourse relations}
}

@article{10.1109/TASLP.2015.2434212,
author = {Spa, Carlos and Rey, Ant\'{o}n and Hern\'{a}ndez, Erwin},
title = {A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics Applications},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2434212},
doi = {10.1109/TASLP.2015.2434212},
abstract = {In recent years, computational engineering has undergone great changes due to the development of the graphics processing unit (GPU) technology. For example, in room acoustics, the wave-based methods, that formerly were considered too expensive for 3-D impulse response simulations, are now chosen to exploit the parallel nature of GPU devices considerably reducing the execution time of the simulations. There exist contributions related to this topic that have explored the performance of different GPU algorithms; however, the computational analysis of a general explicit model that incorporates algorithms with different neighboring orders and a general frequency dependent impedance boundary model has not been properly developed. In this paper, we present a GPU implementation of a complete room acoustic model based on a family of explicit finite-difference time-domain (FDTD) algorithms. We first develop a strategy for implementing a frequency independent (FI) impedance model which is free from thread divergences and then, we extend the model adding a digital impedance filter (DIF) boundary subroutine able to compute the acoustic pressure of different nodes such as corners or edges without an additional performance penalty. Both implementations are validated and deeply analyzed by performing different 3-D numerical experiments. Finally, we define a performance metric which is able to objectively measure the computing throughput of a FDTD implementation using a simple number. The robustness of this metric allows us to compare algorithms even if these have been run in different GPU cards or have been formulated with other explicit models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1368–1380},
numpages = {13},
keywords = {graphics processing unit (GPU), room acoustics, digital impedance filter, explicit compact finite-difference time-domain (FDTD)}
}

@article{10.1109/TASLP.2015.2430815,
author = {Dimitriadis, Dimitrios and Bocchieri, Enrico},
title = {Use of Micro-Modulation Features in Large Vocabulary Continuous Speech Recognition Tasks},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2430815},
doi = {10.1109/TASLP.2015.2430815},
abstract = {Most of the state-of-the-art ASR systems take as input a single type of acoustic features, dominated by the traditional feature schemes, i.e., MFCCs or PLPs. However, these features cannot model rapid, intra-frame phenomena present in the actual speech signals. On the other hand, micro-modulation components, inspired by the AM-FM speech model, can capture these important characteristics of spoken speech, resulting in significant performance improvements, as previously shown in small-vocabulary ASR tasks. Yet, they have limited use in large vocabulary ASR applications, where feature post-processing schemes are usually employed. To enable the successful application of these frequency measures in real-life tasks, we investigate their combination with the traditional Cepstral features when employing linear, e.g., HDA, and nonlinear, i.e., bottleneck neural net (BN), feature transforms. This feature combination is investigated in the context of the hybrid DNN-HMM framework, as well. The experimental results reveal that the integration of micro-modulation and Cepstral features, using neural nets, can greatly improve the ASR performance with respect to using the Cepstral features alone. We apply this novel feature extraction approach on different tasks, i.e., a clean speech task (DARPA-WSJ), the Aurora- 4 task and a real-life, open-vocabulary, mobile search task, the Speak4it, always reporting improved performance, while the obtained relative word error reduction ranges between 7%-21% depending on the task, e.g., a relative WER improvement of 18% for the Speak4it task, and similar improvements, up to 21%, for the WSJ task are reported.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1348–1357},
numpages = {10},
keywords = {robustness, neural networks, speech recognition, feature extraction, speech processing}
}

@article{10.1109/TASLP.2015.2430820,
author = {Mowlaee, Pejman and Kulmer, Josef},
title = {Phase Estimation in Single-Channel Speech Enhancement: Limits-Potential},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2430820},
doi = {10.1109/TASLP.2015.2430820},
abstract = {In this paper, we present an overview on the previous and recent methods proposed to estimate a clean spectral phase from a noisy observation in the context of single-channel speech enhancement. The importance of phase estimation in speech enhancement is inspired by the recent reports on its usefulness in finding a phase-sensitive amplitude estimation. We present a comparative study of the recent phase estimation methods and elaborate their limits. We propose a new phase enhancement method relying on phase decomposition and time-frequency smoothing filters. We demonstrate that the proposed time-frequency phase smoothing method successfully reduces the variance of the noisy phase at harmonics. Our results on different speech and noise databases and different signal-to-noise ratios show that in contrast to the existing benchmark methods only the proposed method balances a tradeoff between a joint improvement in perceived quality of 0.2 in PESQ score and speech intelligibility of 2% by phase-only enhancement.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1283–1294},
numpages = {12},
keywords = {phase estimation, speech intelligibility, perceived quality, speech enhancement, signal reconstruction}
}

@article{10.1109/TASLP.2015.2431851,
author = {Sugiura, Ryosuke and Kamamoto, Yutaka and Harada, Noboru and Kameoka, Hirokazu and Moriya, Takehiro},
title = {Optimal Coding of Generalized-Gaussian-Distributed Frequency Spectra for Low-Delay Audio Coder with Powered All-Pole Spectrum Estimation},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2431851},
doi = {10.1109/TASLP.2015.2431851},
abstract = {We present an optimal coding scheme that parameterizes the maximum-likelihood estimate of variance for frequency spectra belonging to the generalized Gaussian distribution, the distribution covering the Laplacian and the Gaussian. By slightly modifying the all-pole model of the conventional linear prediction (LP), we can estimate the variance with the same method as in LP, which has low computational costs. Experimental results show that incorporating the coding scheme in a state-of-the-art wide-band audio coder enhances its objective and subjective quality in a low-bit-rate and low-delay situation by increasing the compression efficiency. Thus, this coding scheme will be useful in applications like mobile communications, which requires highly efficient compression.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1309–1321},
numpages = {13},
keywords = {linear prediction, generalized Gaussian distribution, audio compression, arithmetic coding, transform coded excitation, low delay}
}

@article{10.1109/TASLP.2015.2425213,
author = {Koldovsk\'{y}, Zbyn\v{e}k and M\'{a}lek, Ji\v{r}\'{\i} and Gannot, Sharon},
title = {Spatial Source Subtraction Based on Incomplete Measurements of Relative Transfer Function},
year = {2015},
issue_date = {August 2015},
publisher = {IEEE Press},
volume = {23},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2425213},
doi = {10.1109/TASLP.2015.2425213},
abstract = {Relative impulse responses between microphones are usually long and dense due to the reverberant acoustic environment. Estimating them from short and noisy recordings poses a long-standing challenge of audio signal processing. In this paper, we apply a novel strategy based on ideas of compressed sensing. Relative transfer function (RTF) corresponding to the relative impulse response can often be estimated accurately from noisy data but only for certain frequencies. This means that often only an incomplete measurement of the RTF is available. A complete RTF estimate can be obtained through finding its sparsest representation in the time-domain: that is, through computing the sparsest among the corresponding relative impulse responses. Based on this approach, we propose to estimate the RTF from noisy data in three steps. First, the RTF is estimated using any conventional method such as the nonstationarity-based estimator by Gannot et al. or through blind source separation. Second, frequencies are determined for which the RTF estimate appears to be accurate. Third, the RTF is reconstructed through solving a weighted l1 convex program, which we propose to solve via a computationally efficient variant of the SpaRSA (Sparse Reconstruction by Separable Approximation) algorithm. An extensive experimental study with real-world recordings has been conducted. It has been shown that the proposed method is capable of improving many conventional estimators used as the first step in most situations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1335–1347},
numpages = {13},
keywords = {l1 norm, relative impulse response, relative transfer function (RTF), sparse approximations, compressed sensing}
}

@article{10.1109/TASLP.2015.2422576,
author = {Meyer, Thomas and Hajlaoui, Najeh and Popescu-Belis, Andrei},
title = {Disambiguating Discourse Connectives for Statistical Machine Translation},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2422576},
doi = {10.1109/TASLP.2015.2422576},
abstract = {This paper shows that the automatic labeling of discourse connectives with the relations they signal, prior to machine translation (MT), can be used by phrase-based statistical MT systems to improve their translations. This improvement is demonstrated here when translating from English to four target languages-French, German, Italian and Arabic-using several test sets from recent MT evaluation campaigns. Using automatically labeled data for training, tuning and testing MT systems is beneficial on condition that labels are sufficiently accurate, typically above 70%. To reach such an accuracy, a large array of features for discourse connective labeling (morpho-syntactic, semantic and discursive) are extracted using state-of-the-art tools and exploited in factored MT models. The translation of connectives is improved significantly, between 0.7% and 10% as measured with the dedicated ACT metric. The improvements depend mainly on the level of ambiguity of the connectives in the test sets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1184–1197},
numpages = {14},
keywords = {discourse connectives, machine translation (MT)}
}

@article{10.1109/TASLP.2015.2419976,
author = {McVicar, Matt and Fukayama, Satoru and Goto, Masataka},
title = {AutoGuitarTab: Computer-Aided Composition of Rhythm and Lead Guitar Parts in the Tablature Space},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2419976},
doi = {10.1109/TASLP.2015.2419976},
abstract = {We present AutoGuitarTab, a system for generating realistic guitar tablature given an input symbolic chord and key sequence. Our system consists of two modules: AutoRhythm-Guitar and AutoLeadGuitar. The first of these generates rhythm guitar tablatures which outline the input chord sequence in a particular style (using Markov chains to ensure playability) and performs a structural analysis to produce a structurally consistent composition. AutoLeadGuitar generates lead guitar parts in distinct musical phrases, guiding the pitch classes towards chord tones and steering the evolution of the rhythmic and melodic intensity according to user preference. Experimentally, we uncover musician-specific trends in guitar playing style, and demonstrate our system's ability to produce playable, realistic and style-specific tablature using a combination of algorithmic, user-surveyed and expert evaluation techniques.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1105–1117},
numpages = {13},
keywords = {symbolic music processing, computer-aided composition (CAC), algorithmic composition}
}

@article{10.1109/TASLP.2015.2425214,
author = {Yeh, Ching-Feng and Lee, Lin-Shan},
title = {An Improved Framework for Recognizing Highly Imbalanced Bilingual Code-Switched Lectures with Cross-Language Acoustic Modeling and Frame-Level Language Identification},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2425214},
doi = {10.1109/TASLP.2015.2425214},
abstract = {This paper considers the recognition of a widely observed type of bilingual code-switched speech: the speaker speaks primarily the host language (usually his native language), but with a few words or phrases in the guest language (usually his second language) inserted in many utterances of the host language. In this case, not only the languages are switched back and forth within an utterance so the language identification is difficult, but much less data are available for the guest language, which results in poor recognition accuracy for the guest language part. Unit merging approaches on three levels of acoustic modeling (triphone models, HMM states and Gaussians) have been proposed for cross-lingual data sharing for such highly imbalanced bilingual code-switched speech. In this paper, we present an improved overall framework on top of the previously proposed unit merging approaches for recognizing such code-switched speech. This includes unit recovery for reconstructing the identity for units of the two languages after being merged, unit occupancy ranking to offer much more flexible data sharing between units both across languages and within the language based on the accumulated occupancy of the HMM states, and estimation of frame-level language posteriors using blurred posteriorgram features (BPFs) to be used in decoding. We also present a complete set of experimental results comparing all approaches involved for a real-world application scenario under unified conditions, and show very good improvement achieved with the proposed approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1144–1159},
numpages = {16},
keywords = {speech recognition, language identification, cross-language acoustic modeling, code-switching, bilingual}
}

@article{10.1109/TASLP.2015.2425220,
author = {Wang, Rui and Zhao, Hai and Lu, Bao-Liang and Utiyama, Masao and Sumita, Eiichiro},
title = {Bilingual Continuous-Space Language Model Growing for Statistical Machine Translation},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2425220},
doi = {10.1109/TASLP.2015.2425220},
abstract = {Larger n-gram language models (LMs) perform better in statistical machine translation (SMT). However, the existing approaches have two main drawbacks for constructing larger LMs: 1) it is not convenient to obtain larger corpora in the same domain as the bilingual parallel corpora in SMT; 2) most of the previous studies focus on monolingual information from the target corpora only, and redundant n-grams have not been fully utilized in SMT. Nowadays, continuous-space language model (CSLM), especially neural network language model (NNLM), has been shown great improvement in the estimation accuracies of the probabilities for predicting the target words. However, most of these CSLM and NNLM approaches still consider monolingual information only or require additional corpus. In this paper, we propose a novel neural network based bilingual LM growing method. Compared to the existing approaches, the proposed method enables us to use bilingual parallel corpus for LM growing in SMT. The results show that our new method outperforms the existing approaches on both SMT performance and computational efficiency significantly.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1209–1220},
numpages = {12},
keywords = {statistical machine translation (SMT), language model growing (LMG), continuous-space language model, neural network language model}
}

@article{10.5555/2876428.2876438,
author = {Chong, Tze Yuang and Banchs, Rafael E. and Chng, Eng Siong and Li, Haizhou},
title = {Decoupling Word-Pair Distance and Co-Occurrence Information for Effective Long History Context Language Modeling},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
abstract = {In this paper, we propose the use of distance and co-occurrence information of word-pairs to improve language modeling. We have empirically shown that, for history-context sizes of up to ten words, the extracted information about distance and co-occurrence complements the n-gram language model well, for which learning long-history contexts is inherently difficult. Evaluated on the Wall Street Journal and the Switchboard corpora, our proposed model reduces the trigram model perplexity by up to 11.2% and 6.5%, respectively. As compared to the distant bigram model and the trigger model, our proposed model offers a more effective manner of capturing far context information, as verified in terms of perplexity and computational efficiency, i.e., fewer free parameters to be fine-tuned. Experiments using the proposed model for speech recognition, text classification and word prediction tasks showed improved performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1221–1232},
numpages = {12},
keywords = {language modeling, speech recognition, text categorization}
}

@article{10.5555/2876428.2876436,
author = {Remes, Ulpu and L\'{o}pez, Ana Ram\'{\i}rez and Palom\"{a}ki, Kalle and Kurimo, Mikko},
title = {Bounded Conditional Mean Imputation with Observation Uncertainties and Acoustic Model Adaptation},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
abstract = {Automatic speech recognition systems use noise compensation and acoustic model adaptation to increase robustness towards speaker and environmental variation. The current work focuses on noise compensation with bounded conditional mean imputation (BCMI). BCMI approaches are missing-data methods which operate on the assumption that noise-corrupted observations can be divided into reliable and unreliable components. BCMI methods substitute the unreliable components with a clean speech posterior distribution. The posterior means can be used as clean speech estimates and the posterior variances can be introduced in acoustic model likelihood calculation as observation uncertainties. In addition, we propose in the current work that similar uncertainties are introduced in acoustic model adaptation. Evaluation with speech data recorded in diverse public and car environments indicates that the proposed uncertainties improve adaptation performance. When uncertainties were used in acoustic model likelihood calculation and adaptation, the proposed imputation and adaptation system introduced 15%-84% relative error reductions to an uncompensated baseline system performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1198–1208},
numpages = {11},
keywords = {noiserobust speech recognition, acoustic model adaptation, observation uncertainties, missing data}
}

@article{10.1109/TASLP.2015.2425219,
author = {Marelli, Dami\'{a}n and Baumgartner, Robert and Majdak, Piotr},
title = {Efficient Approximation of Head-Related Transfer Functions in Subbands for Accurate Sound Localization},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2425219},
doi = {10.1109/TASLP.2015.2425219},
abstract = {Head-related transfer functions (HRTFs) describe the acoustic filtering of incoming sounds by the human morphology and are essential for listeners to localize sound sources in virtual auditory displays. Since rendering complex virtual scenes is computationally demanding, we propose four algorithms for efficiently representing HRTFs in subbands, i.e., as an analysis filterbank (FB) followed by a transfer matrix and a synthesis FB. All four algorithms use sparse approximation procedures to minimize the computational complexity while maintaining perceptually relevant HRTF properties. The first two algorithms separately optimize the complexity of the transfer matrix associated to each HRTF for fixed FBs. The other two algorithms jointly optimize the FBs and transfer matrices for complete HRTF sets by two variants. The first variant aims at minimizing the complexity of the transfer matrices, while the second one does it for the FBs. Numerical experiments investigate the latency-complexity trade-off and show that the proposed methods offer significant computational savings when compared with other available approaches. Psychoacoustic localization experiments were modeled and conducted to find a reasonable approximation tolerance so that no significant localization performance degradation was introduced by the subband representation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1130–1143},
numpages = {14},
keywords = {sparse approximation, virtual acoustics, head-related transfer functions (HRTFs), sound localization, subband signal processing}
}

@article{10.1109/TASLP.2015.2427520,
author = {Sun, Meng and Li, Yinan and Gemmeke, Jort F. and Zhang, Xiongwei},
title = {Speech Enhancement under Low SNR Conditions via Noise Estimation Using Sparse and Low-Rank NMF with Kullback–Leibler Divergence},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2427520},
doi = {10.1109/TASLP.2015.2427520},
abstract = {A key stage in speech enhancement is noise estimation which usually requires prior models for speech or noise or both. However, prior models can sometimes be difficult to obtain. In this paper, without any prior knowledge of speech and noise, sparse and low-rank nonnegative matrix factorization (NMF) with Kullback-Leibler divergence is proposed to noise and speech estimation by decomposing the input noisy magnitude spectrogram into a low-rank noise part and a sparse speech-like part. This initial unsupervised speech-noise estimation allows us to set a subsequent regularized version of NMF or convolutional NMF to reconstruct the noise and speech spectrogram, either by estimating a speech dictionary on the fly (categorized as unsupervised approaches) or by using a pre-trained speech dictionary on utterances with disjoint speakers (categorized as semi-supervised approaches). Information fusion was investigated by taking the geometric mean of the outputs from multiple enhancement algorithms. The performance of the algorithms were evaluated on five metrics (PESQ, SDR, SNR, STOI, and OVERALL) by making experiments on TIMIT with 15 noise types. The geometric means of the proposed unsupervised approaches outperformed spectral subtraction (SS), minimum mean square estimation (MMSE) under low input SNR conditions. All the proposed semi-supervised approaches showed superiority over SS and MMSE and also obtained better performance than the state-of-the-art algorithms which utilized a prior noise or speech dictionary under low SNR conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1233–1242},
numpages = {10},
keywords = {nonnegative, speech enhancement, blockwise/convolutional, matrix factorization, sparse and low-rank decomposition}
}

@article{10.1109/TASLP.2015.2422573,
author = {Chen, Dongpeng and Mak, Brian Kan-Wing},
title = {Multitask Learning of Deep Neural Networks for Low-Resource Speech Recognition},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2422573},
doi = {10.1109/TASLP.2015.2422573},
abstract = {We propose a multitask learning (MTL) approach to improve low-resource automatic speech recognition using deep neural networks (DNNs) without requiring additional language resources. We first demonstrate that the performance of the phone models of a single low-resource language can be improved by training its grapheme models in parallel under the MTL framework. If multiple low-resource languages are trained together, we investigate learning a set of universal phones (UPS) as an additional task again in the MTL framework to improve the performance of the phone models of all the involved languages. In both cases, the heuristic guideline is to select a task that may exploit extra information from the training data of the primary task(s). In the first method, the extra information is the phone-to-grapheme mappings, whereas in the second method, the UPS helps to implicitly map the phones of the multiple languages among each other. In a series of experiments using three low-resource South African languages in the Lwazi corpus, the proposed MTL methods obtain significant word recognition gains when compared with single-task learning (STL) of the corresponding DNNs or ROVER that combines results from several STL-trained DNNs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1172–1183},
numpages = {12},
keywords = {universal grapheme set, universal phone set, low-resource speech recognition, multitask learning, deep neural network (DNN)}
}

@article{10.1109/TASLP.2015.2419972,
author = {Basaran, Doundefineda\c{c} and Cemgil, Ali Taylan and Anarim, Emin},
title = {A Probabilistic Model-Based Approach for Aligning Multiple Audio Sequences},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2419972},
doi = {10.1109/TASLP.2015.2419972},
abstract = {We formulate the alignment problem of multiple and partially overlapping audio sequences in a probabilistic framework. We define and compare five generative models for several time varying features extracted from audio clips that are recorded independently and asynchronously. For each model, we derive the associated scoring function that evaluates the quality of an alignment. The matching is then achieved with a sequential algorithm. The derived score functions are also able to identify the cases where the sequences do not overlap and handle multiple sequences where no sequence is covering the entire timeline. The simulation results on real data suggest that the approach is able to handle difficult, ambiguous scenarios and partial matchings where simple baseline methods such as correlation fail.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1160–1171},
numpages = {12},
keywords = {audio synchronization, probabilistic model, fingerprinting, audio alignment, audio matching, multi sequence alignment}
}

@article{10.1109/TASLP.2015.2419978,
author = {Van Segbroeck, Maarten and Travadi, Ruchir and Narayanan, Shrikanth S.},
title = {Rapid Language Identification},
year = {2015},
issue_date = {July 2015},
publisher = {IEEE Press},
volume = {23},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2419978},
doi = {10.1109/TASLP.2015.2419978},
abstract = {A critical challenge to automatic language identification (LID) is achieving accurate performance with the shortest possible speech segment in a rapid fashion. The accuracy to correctly identify the spoken language is highly sensitive to the duration of speech and is bounded by the amount of information available. The proposed approach for rapid language identification transforms the utterances to a low dimensional i-vector representation upon which language classification methods are applied. In order to meet the challenges involved in rapidly making reliable decisions about the spoken language, a highly accurate and computationally efficient framework of i-vector extraction is proposed. The LID framework integrates the approach of universal background model (UBM) fused total variability modeling. UBM-fused modeling yields the estimation of a more discriminant, single i-vector space. This way, it is also a computationally more efficient alternative than system level fusion. A further reduction in equal error rate is achieved by training the i-vector model on long duration speech utterances and by the deployment of a robust feature extraction scheme that aims to capture the relevant language cues under various acoustic conditions. Evaluation results on the DARPA RATS data corpus suggest the potential of performing successful automated language identification at the level of one second of speech or even shorter duration.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1118–1129},
numpages = {12},
keywords = {total variability modeling, noise robustness, short-duration speech, universal background model (UBM) fusion, I-vector, rapid language identification}
}

@article{10.1109/TASLP.2015.2414820,
author = {Liu, Shih-Hung and Chen, Kuan-Yu and Chen, Berlin and Wang, Hsin-Min and Yen, Hsu-Chun and Hsu, Wen-Lian},
title = {Combining Relevance Language Modeling and Clarity Measure for Extractive Speech Summarization},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2414820},
doi = {10.1109/TASLP.2015.2414820},
abstract = {Extractive speech summarization, which purports to select an indicative set of sentences from a spoken document so as to succinctly represent the most important aspects of the document, has garnered much research over the years. In this paper, we cast extractive speech summarization as an ad-hoc information retrieval (IR) problem and investigate various language modeling (LM) methods for important sentence selection. The main contributions of this paper are four-fold. First, we explore a novel sentence modeling paradigm built on top of the notion of relevance, where the relationship between a candidate summary sentence and a spoken document to be summarized is discovered through different granularities of context for relevance modeling. Second, not only lexical but also topical cues inherent in the spoken document are exploited for sentence modeling. Third, we propose a novel clarity measure for use in important sentence selection, which can help quantify the thematic specificity of each individual sentence that is deemed to be a crucial indicator orthogonal to the relevance measure provided by the LM-based methods. Fourth, in an attempt to lessen summarization performance degradation caused by imperfect speech recognition, we investigate making use of different levels of index features for LM-based sentence modeling, including words, subword-level units, and their combination. Experiments on broadcast news summarization seem to demonstrate the performance merits of our methods when compared to several existing well-developed and/or state-of-the-art methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {957–969},
numpages = {13},
keywords = {language modeling, relevance modeling, speech summarization, KL divergence, clarity measure}
}

@article{10.1109/TASLP.2014.2387411,
author = {Rouvier, Mickael and Oger, Stanislas and Linar\`{e}s, Georges and Matrouf, Driss and Merialdo, Bernard and Li, Yingbo},
title = {Audio-Based Video Genre Identification},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2387411},
doi = {10.1109/TASLP.2014.2387411},
abstract = {This paper presents investigations about the automatic identification of video genre by audio channel analysis. Genre refers to editorial styles such commercials, movies, sports... We propose and evaluate some methods based on both low and high level descriptors, in cepstral or time domains, but also by analyzing the global structure of the document and the linguistic contents. Then, the proposed features are combined and their complementarity is evaluated. On a database composed of single-stories web-videos, the best audio-only based system performs 9% of Classification Error Rate (CER). Finally, we evaluate the complementarity of the proposed audio features and video features that are classically used for Video Genre Identification (VGI). Results demonstrate the complementarity of the modalities for genre recognition, the final audio-video system reaching 6% CER.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1031–1041},
numpages = {11},
keywords = {linguistic feature extraction, video genre classification, automatic classification}
}

@article{10.1109/TASLP.2015.2418576,
author = {Kameoka, Hirokazu and Yoshizato, Kota and Ishihara, Tatsuma and Kadowaki, Kento and Ohishi, Yasunori and Kashino, Kunio},
title = {Generative Modeling of Voice Fundamental Frequency Contours},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2418576},
doi = {10.1109/TASLP.2015.2418576},
abstract = {This paper introduces a generative model of voice fundamental frequency (F0) contours that allows us to extract prosodic features from raw speech data. The present contour model is formulated by translating the Fujisaki model, a well-founded mathematical model representing the control mechanism of vocal fold vibration, into a probabilistic model described as a discrete-time stochastic process. There are two motivations behind this formulation. One is to derive a general parameter estimation framework for the Fujisaki model that allows the introduction of powerful statistical methods. The other is to construct an automatically trainable version of the Fujisaki model that we can incorporate into statistical-model-based text-to-speech synthesizers in such a way that the Fujisaki-model parameters can be learned from a speech corpus in a unified manner. It could also be useful for other speech applications such as emotion recognition, speaker identification, speech conversion and dialogue systems, in which prosodic information plays a significant role. We quantitatively evaluated the performance of the proposed Fujisaki model parameter extractor using real speech data. Experimental results revealed that our method was superior to a state-of-the-art Fujisaki model parameter extractor.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1042–1053},
numpages = {12},
keywords = {prosody, Fujisaki model, voice fundamental frequency contour, expectation-maximization algorithm}
}

@article{10.1109/TASLP.2015.2416655,
author = {Foster, Peter and Dixon, Simon and Klapuri, Anssi},
title = {Identifying Cover Songs Using Information-Theoretic Measures of Similarity},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2416655},
doi = {10.1109/TASLP.2015.2416655},
abstract = {This paper investigates methods for quantifying similarity between audio signals, specifically for the task of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantized audio features, to continuous-valued approaches. In the discrete case, we propose a method for computing the normalized compression distance, where we account for correlation between time series. In the continuous case, we propose to compute information-based measures of similarity as statistics of the prediction error between time series. We evaluate our methods on two cover song identification tasks using a data set comprised of 300 Jazz standards and using the Million Song Dataset. For both datasets, we observe that continuous-valued approaches outperform discrete-valued approaches. We consider approaches to estimating the normalized compression distance (NCD) based on string compression and prediction, where we observe that our proposed normalized compression distance with alignment (NCDA) improves average performance over NCD, for sequential compression algorithms. Finally, we demonstrate that continuous-valued distances may be combined to improve performance with respect to baseline approaches. Using a large-scale filter-and-refine approach, we demonstrate state-of-the-art performance for cover song identification using the Million Song Dataset.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {993–1005},
numpages = {13},
keywords = {time series prediction, normalized compression distance, audio similarity measures, cover song identification}
}

@article{10.1109/TASLP.2015.2418577,
author = {Cernak, Milos and Garner, Philip N. and Lazaridis, Alexandros and Motlicek, Petr and Na, Xingyu},
title = {Incremental Syllable-Context Phonetic Vocoding},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2418577},
doi = {10.1109/TASLP.2015.2418577},
abstract = {Current very low bit rate speech coders are, due to complexity limitations, designed to work off-line. This paper investigates incremental speech coding that operates real-time and incrementally (i.e., encoded speech depends only on already-uttered speech without the need of future speech information). Since human speech communication is asynchronous (i.e., different information flows being simultaneously processed), we hypothesized that such an incremental speech coder should also operate asynchronously. To accomplish this task, we describe speech coding that reflects the human cortical temporal sampling that packages information into units of different temporal granularity, such as phonemes and syllables, in parallel. More specifically, a phonetic vocoder--cascaded speech recognition and synthesis systems--extended with syllable-based information transmission mechanisms is investigated. There are two main aspects evaluated in this work, the synchronous and asynchronous coding. Synchronous coding refers to the case when the phonetic vocoder and speech generation process depend on the syllable boundaries during encoding and decoding respectively. On the other hand, asynchronous coding refers to the case when the phonetic encoding and speech generation processes are done independently of the syllable boundaries. Our experiments confirmed that the asynchronous incremental speech coding performs better, in terms of intelligibility and overall speech quality, mainly due to better alignment of the segmental and prosodic information. The proposed vocoding operates at an uncompressed bit rate of 213 bits/sec and achieves an average communication delay of 243 ms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1019–1030},
numpages = {12},
keywords = {very low bit rate speech coding, parametric speech synthesis}
}

@article{10.1109/TASLP.2015.2419980,
author = {Jia, Maoshen and Yang, Ziyu and Bao, Changchun and Zheng, Xiguang and Ritz, Christian},
title = {Encoding Multiple Audio Objects Using Intra-Object Sparsity},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2419980},
doi = {10.1109/TASLP.2015.2419980},
abstract = {Preserving audio scenes in the form of audio objects has become common in recent years. Object-based audio techniques provide more flexibility for personalized rendering as well as a more accurate audio object trajectory. For encoding and transmitting multiple audio objects in a lossy manner, a new compression framework for multiple simultaneously occurring audio objects is presented in this work. The proposed encoding approach is based on the intra-object sparsity (approximate k-sparsity). After establishing a quantitative measure of approximate k-sparsity, statistical analysis is employed to validate the proposed intra-object sparsity of audio objects. By exploring this intra-object sparsity, multiple simultaneously occurring audio objects are compressed into a mono downmix signal with side information. This downmix signal can be further compressed by legacy audio codecs. Meanwhile, the side information is transmitted in a lossless manner. The objective and subjective evaluations revealed that the proposed compression framework achieved better perceptual quality compared to an existing technique where up to eight audio objects are considered. The subjective evaluations also confirmed that the proposed approach is able to achieve scalable transmission according to the bandwidth while preserving the perceptual quality of both the individual audio objects and the spatial audio scenes.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1082–1095},
numpages = {14},
keywords = {sparsity, audio object coding, multichannel audio compression}
}

@article{10.5555/2876416.2876419,
author = {Han, Kun and Wang, Yuxuan and Wang, DeLiang and Woods, William S. and Merks, Ivo and Zhang, Tao},
title = {Learning Spectral Mapping for Speech Dereverberation and Denoising},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
abstract = {In real-world environments, human speech is usually distorted by both reverberation and background noise, which have negative effects on speech intelligibility and speech quality. They also cause performance degradation in many speech technology applications, such as automatic speech recognition. Therefore, the dereverberation and denoising problems must be dealt with in daily listening environments. In this paper, we propose to perform speech dereverberation using supervised learning, and the supervised approach is then extended to address both dereverberation and denoising. Deep neural networks are trained to directly learn a spectral mapping from the magnitude spectrogram of corrupted speech to that of clean speech. The proposed approach substantially attenuates the distortion caused by reverberation, as well as background noise, and is conceptually simple. Systematic experiments show that the proposed approach leads to significant improvements of predicted speech intelligibility and quality, as well as automatic speech recognition in reverberant noisy conditions. Comparisons show that our approach substantially outperforms related methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {982–992},
numpages = {11},
keywords = {dereverberation, deep neural networks (DNNs), supervised learning, spectral mapping, denoising}
}

@article{10.1109/TASLP.2015.2414823,
author = {Niedzwiecki, Maciej and Cio\l{}ek, Marcin and Cisowski, Krzysztof},
title = {Elimination of Impulsive Disturbances from Stereo Audio Recordings Using Vector Autoregressive Modeling and Variable-Order Kalman Filtering},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2414823},
doi = {10.1109/TASLP.2015.2414823},
abstract = {This paper presents a new approach to elimination of impulsive disturbances from stereo audio recordings. The proposed solution is based on vector autoregressive modeling of audio signals. Online tracking of signal model parameters is performed using the exponentially weighted least squares algorithm. Detection of noise pulses and model-based interpolation of the irrevocably distorted samples is realized using an adaptive, variable-order Kalman filter. The proposed approach is evaluated on a set of clean audio signals contaminated with real click waveforms extracted from old gramophone recordings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {970–981},
numpages = {12},
keywords = {vector autoregressive models, adaptive Kalman filtering, elimination of impulsive disturbances}
}

@article{10.1109/TASLP.2015.2418571,
author = {Schwarz, Andreas and Kellermann, Walter},
title = {Coherent-to-Diffuse Power Ratio Estimation for Dereverberation},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2418571},
doi = {10.1109/TASLP.2015.2418571},
abstract = {The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1006–1018},
numpages = {13},
keywords = {spatial coherence, reverberation suppression, diffuseness, diffuse noise suppression, dereverberation}
}

@article{10.1109/TASLP.2015.2419971,
author = {Bates, Alice P. and Khalid, Zubair and Kennedy, Rodney A.},
title = {Novel Sampling Scheme on the Sphere for Head-Related Transfer Function Measurements},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2419971},
doi = {10.1109/TASLP.2015.2419971},
abstract = {This paper presents a novel sampling scheme on the sphere for obtaining head-related transfer function (HRTF) measurements and accurately computing the spherical harmonic transform (SHT). The scheme requires an optimal number of samples, given by the degrees of freedom in the spectral domain, for the accurate representation of the HRTF that is band-limited in the spherical harmonic domain. The proposed scheme allows for the samples to be easily taken over the sphere due to its iso-latitude structure and non-dense sampling near the poles. In addition, the scheme can be used when samples are not taken from the south polar cap region of the sphere as the HRTF measurements are not reliable in south polar cap region due to reflections from the ground. Furthermore, the scheme has a hierarchical structure, which enables the HRTF to be analyzed at different audible frequencies using the same sampling configuration. In comparison to the proposed scheme, none of the other sampling schemes on the sphere simultaneously possess all these properties. We conduct several numerical experiments to determine the accuracy of the SHT associated with the proposed sampling scheme. We show that the SHT attains accuracy on the order of numerical precision (10-14) when samples are taken over the whole sphere, both in the optimal sample placement and hierarchical configurations, and achieves an acceptable level of accuracy (10-5) when samples are not taken over the south polar cap region of the sphere for the band-limits of interest. Simulations are used to show the accurate reconstruction of the HRTF over the whole sphere, including unmeasured locations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1068–1081},
numpages = {14},
keywords = {spherical harmonics, sampling, spectral analysis, 2-sphere (unit sphere), spherical harmonic transform, head-related transfer function (HRTF) measurements}
}

@article{10.1109/TASLP.2015.2419076,
author = {Markovic, Dejan and Antonacci, Fabio and Sarti, Augusto and Tubaro, Stefano},
title = {Multiview Soundfield Imaging in the Projective Ray Space},
year = {2015},
issue_date = {June 2015},
publisher = {IEEE Press},
volume = {23},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2419076},
doi = {10.1109/TASLP.2015.2419076},
abstract = {A soundfield image is a data structure that efficiently encodes and represents the wave field as captured by a microphone array. Its representation is based on the directional plenacoustic function, which is defined as the radiance of the acoustic paths (rays) that cross the segment that the array lies upon. The soundfield image can be processed "as is" to develop a variety of applications. In its original formulation, the soundfield image is based on a Euclidean parameterization that can accommodate a limited range of rays and is suitable for managing a single array only. In this paper, we generalize this methodology to the case of multiple microphone arrays deployed in space. The use of multiple arrays allows us to capture truly global information on the sound field but requires us to rethink the ray space, and adopt a global representation of the acoustic rays based on projective geometry. After introducing the new parameterization, we present two examples of applications: the estimation of the mutual poses of two or more arrays (self-calibration); and the localization of multiple acoustic

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repo_name: goyalkaraniit/acm-cr