text:
conventional MMSE based technique. It is also interesting to observe that the proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general. Furthermore, the resulting DNN model, trained with artificial synthesized data, is also effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {7–19},
numpages = {13},
keywords = {non-stationary noise, noise aware training, deep neural networks (DNNs), global variance equalization, speech enhancement, noise reduction, dropout}
}

@article{10.1109/TASLP.2014.2375575,
author = {Rakotomamonjy, Alain and Gasso, Gilles},
title = {Histogram of Gradients of Time-Frequency Representations for Audio Scene Classification},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2375575},
doi = {10.1109/TASLP.2014.2375575},
abstract = {This abstract presents our entry to the Detection and Classification of Acoustic Scenes challenge. The approach we propose for classifying acoustic scenes is based on transforming the audio signal into a time-frequency representation and then in extracting relevant features about shapes and evolutions of time-frequency structures. These features are based on histogram of gradients that are subsequently fed to amulti-class linear support vector machines.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {142–153},
numpages = {12},
keywords = {histogram of gradient, constant Q transform, support vector machines}
}

@article{10.1109/TASLP.2014.2372313,
author = {Ahani, Soodeh and Ghaemmaghami, Shahrokh and Wang, Z. Jane},
title = {A Sparse Representation-Based Wavelet Domain Speech Steganography Method},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2372313},
doi = {10.1109/TASLP.2014.2372313},
abstract = {In this paper, we present a novel speech steganography method using discrete wavelet transform and sparse decomposition to address the undetectability concern in speech steganography. The proposed speech steganography method exploits the sparse representation to embed secret messages into higher semantic levels of the cover signal, resulting in increased undetectability. The proposed method also yields improvements on both stego signal quality and embedding capacity, which are the two major requirements of a steganography algorithm. Our experimental results illustrate that the stego signals generated by the proposed method are perceptually indistinguishable from the original cover signals, quantified by both SNR and PESQ quality measures. When compared with two well-known steganography methods, the proposed method is shown to be superior on addressing major requirements of a steganography algorithm, imperceptibility, undetectability, and capacity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {80–91},
numpages = {12},
keywords = {dictionary learning, speech steganography, discrete wavelet transform, data hiding, sparse representation}
}

@article{10.1109/TASLP.2014.2372901,
author = {Su, Rongfeng and Liu, Xunying and Wang, Lan},
title = {Automatic Complexity Control of Generalized Variable Parameter HMMs for Noise Robust Speech Recognition},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2372901},
doi = {10.1109/TASLP.2014.2372901},
abstract = {An important part of the acoustic modelling problem for automatic speech recognition (ASR) systems is to handle the mismatch against a target environment created by time-varying external factors such as ambient noise. One possible solution to this problem is to introduce controllability to the underlying acoustic model to allow an instantaneous adaptation to the underlying noise condition. Along this line, the continuous trajectory of optimal, well matched model parameters against the varying noise can be explicitly modelled using, for example, generalized variable parameter HMMs (GVP-HMM). In order to improve the generalization and computational efficiency of conventional GVP-HMMs, this paper investigates a novel model complexity control method for GVP-HMMs. The optimal polynomial degrees of Gaussian mean, variance and model space linear transform trajectories are automatically determined at local level. Significant error rate reductions of 20% and 28% relative were obtained over the multi-style training baseline systems on Aurora 2 and a medium vocabulary Mandarin Chinese speech recognition task respectively. Consistent performance improvements and model size compression of 60% relative were also obtained over the baseline GVP-HMM systems using a uniformly assigned polynomial degree.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {102–114},
numpages = {13},
keywords = {generalized variable parameter HMMs, robust speech recognition, complexity control, variable noise}
}

@article{10.1109/TASLP.2014.2377595,
author = {Mart\'{\i}nez-Hinarejos, Carlos D. and Bened\'{\i}, Jos\'{e}-Miguel and Tamarit, Vicent},
title = {Unsegmented Dialogue Act Annotation and Decoding with N-Gram Transducers},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2377595},
doi = {10.1109/TASLP.2014.2377595},
abstract = {Most studies on dialogue corpora, as well as most dialogue systems, employ dialogue acts as the basic units for interpreting discourse structure, user input and system actions. The definition of the discourse structure and the dialogue strategy consequently require the tagging of dialogue corpora in terms of dialogue acts. The tagging problem presents two basic variants: a batch variant (annotation of whole dialogues, in order to define dialogue strategy or study discourse structure) and an online variant (decoding of the dialogue act sequence of a given turn, in order to interpret user intentions). In the two variants is unusual having the segmentation of each turn into the dialogue meaningful units (segments) to which a dialogue act is assigned. In this paper we present the use of the N-Gram Transducer technique for tagging dialogues, without needing to provide a prior segmentation, in these two different variants (dialogue annotation and turn decoding). Experiments were performed in two corpora of different nature and results show that N-Gram Transducer models are suitable for these tasks and provide good performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {198–211},
numpages = {14},
keywords = {spoken dialogue systems, dialogue annotation, n-gram transducer}
}

@article{10.1109/TASLP.2014.2371544,
author = {Tong, Renjie and Zhou, Yingyue and Zhang, Long and Bao, Guangzhao and Ye, Zhongfu},
title = {A Robust Time-Frequency Decomposition Model for Suppression of Mixed Gaussian-Impulse Noise in Audio Signals},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2371544},
doi = {10.1109/TASLP.2014.2371544},
abstract = {In this paper, we propose a robust time-frequency decomposition (RTFD) model to restore audio signals degraded by sparse impulse noise mixed with small dense Gaussian noise. This kind of noise is very common especially in old-time recordings. The proposed RTFD model is based on the observation that these degraded audio signals mainly contain four parts, i.e., the quasi-periodic and voiced part, the aperiodic and transient part, the arbitrarily large impulse noise and the small dense Gaussian noise. Sparsity and local correlations of corresponding parts are exploited to solve the RTFD model. We also heuristically develop a discriminative orthogonal matching pursuit (DOMP) algorithm to more precisely estimate sparse representing vectors. Specifically, the DOMP algorithm divides the whole atom set into two subsets, i.e., the active subset and the passive subset. Atoms in two subsets are treated discriminatively since sparsity regularization terms are not equally weighted. Based on RTFD and DOMP, we have developed two algorithms, i.e., the fidelity-oriented algorithm and the articulation-oriented algorithm. The proposed algorithms achieve considerable performance on both synthetic and real noisy signals. Results show that the articulation-oriented algorithm using DOMP obviously outperforms other algorithms in heavier impulse noise situations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {69–79},
numpages = {11},
keywords = {Gaussian-impulse noise, articulation-oriented, robust time-frequency decomposition (RTFD), discriminative orthogonal matching pursuit (DOMP), restoration, degraded}
}

@article{10.1109/TASLP.2014.2367814,
author = {Phan, Huy and Maa\ss{}, Marco and Mazur, Radoslaw and Mertins, Alfred},
title = {Random Regression Forests for Acoustic Event Detection and Classification},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2367814},
doi = {10.1109/TASLP.2014.2367814},
abstract = {Despite the success of the automatic speech recognition framework in its own application field, its adaptation to the problem of acoustic event detection has resulted in limited success. In this paper, instead of treating the problem similar to the segmentation and classification tasks in speech recognition, we pose it as a regression task and propose an approach based on random forest regression. Furthermore, event localization in time can be efficiently handled as a joint problem. We first decompose the training audio signals into multiple interleaved superframes which are annotated with the corresponding event class labels and their displacements to the temporal onsets and offsets of the events. For a specific event category, a random-forest regression model is learned using the displacement information. Given an unseen superframe, the learned regressor will output the continuous estimates of the onset and offset locations of the events. To deal with multiple event categories, prior to the category-specific regression phase, a superframe-wise recognition phase is performed to reject the background superframes and to classify the event superframes into different event categories. While jointly posing event detection and localization as a regression problem is novel, the superior performance on two databases ITC-Irst and UPC-TALP demonstrates the efficiency and potential of the proposed approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {20–31},
numpages = {12},
keywords = {random forest, superframe, acoustic event detection, regression forest}
}

@article{10.1109/TASLP.2014.2377591,
author = {Jensen, Jesper and Tan, Zheng-Hua},
title = {Minimum Mean-Square Error Estimation of Mel-Frequency Cepstral Features-a Theoretically Consistent Approach},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2377591},
doi = {10.1109/TASLP.2014.2377591},
abstract = {In this work, we consider the problem of feature enhancement for noise-robust automatic speech recognition (ASR). We propose a method for minimum mean-square error (MMSE) estimation of mel-frequency cepstral features, which is based on a minimum number of well-established, theoretically consistent statistical assumptions. More specifically, the method belongs to the class of methods relying on the statistical framework proposed in Ephraim and Malah's original work ("Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-32, no. 6, 1984). The method is general in that it allows MMSE estimation of mel-frequency cepstral coefficients (MFCC's), cepstral-mean subtracted (CMS-) MFCC's, autoregressive-moving-average (ARMA)-filtered CMS-MFCC's, velocity, and acceleration coefficients. In addition, the method is easily modified to take into account other compressive non-linearities than the logarithm traditionally used for MFCC computation. In terms of MFCC estimation performance, as measured by MFCC mean-square error, the proposed method shows performance which is identical to or better than other state-of-the-art methods. In terms of ASR performance, no statistical difference could be found between the proposed method and the state-of-the-art methods. We conclude that existing state-of-the-art MFCC feature enhancement algorithms within this class of algorithms, while theoretically suboptimal or based on theoretically inconsistent assumptions, perform close to optimally in the MMSE sense.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {186–197},
numpages = {12},
keywords = {mel-frequency cepstral coefficient (MFCC), speech enhancement, robust automatic speech recognition (ASR), minimum mean-square error (MMSE) estimation}
}

@article{10.1109/TASLP.2014.2377581,
author = {Nielsen, Jens Brehm Bagger and Nielsen, Jakob and Larsen, Jan},
title = {Perception-Based Personalization of Hearing Aids Using Gaussian Processes and Active Learning},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2377581},
doi = {10.1109/TASLP.2014.2377581},
abstract = {Personalization of multi-parameter hearing aids involves an initial fitting followed by a manual knowledge-based trial-and-error fine-tuning from ambiguous verbal user feedback. The result is an often suboptimal HA setting whereby the full potential of modern hearing aids is not utilized. This article proposes an interactive hearing-aid personalization system that obtains an optimal individual setting of the hearing aids from direct perceptual user feedback. Results obtained with ten hearing-impaired subjects show that ten to twenty pairwise user assessments between different settings--equivalent to 5-10 min--is sufficient for personalization of up to four hearing-aid parameters. A setting obtained by the system was significantly preferred by the subject over the initial fitting, and the obtained setting could be reproduced with reasonable precision. The system may have potential for clinical usage to assist both the hearing-care professional and the user.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {162–173},
numpages = {12},
keywords = {pairwise comparisons, individualization, Gaussian process (GP), hearing aids, active learning, personalization}
}

@article{10.1109/TASLP.2014.2372314,
author = {Narayanan, Arun and Wang, DeLiang},
title = {Improving Robustness of Deep Neural Network Acoustic Models via Speech Separation and Joint Adaptive Training},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2372314},
doi = {10.1109/TASLP.2014.2372314},
abstract = {Although deep neural network (DNN) acoustic models are known to be inherently noise robust, especially with matched training and testing data, the use of speech separation as a frontend and for deriving alternative feature representations has been shown to improve performance in challenging environments. We first present a supervised speech separation system that significantly improves automatic speech recognition (ASR) performance in realistic noise conditions. The system performs separation via ratio time-frequency masking; the ideal ratio mask (IRM) is estimated using DNNs. We then propose a framework that unifies separation and acoustic modeling via joint adaptive training. Since the modules for acoustic modeling and speech separation are implemented using DNNs, unification is done by introducing additional hidden layers with fixed weights and appropriate network architecture. On the CHiME-2 medium-large vocabulary ASR task, and with log mel spectral features as input to the acoustic model, an independently trained ratio masking frontend improves word error rates by 10.9% (relative) compared to the noisy baseline. In comparison, the jointly trained system improves performance by 14.4%. We also experiment with alternative feature representations to augment the standard log mel features, like the noise and speech estimates obtained from the separation module, and the standard feature set used for IRM estimation. Our best system obtains a word error rate of 15.4% (absolute), an improvement of 4.6 percentage points over the next best result on this corpus.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {92–101},
numpages = {10},
keywords = {time-frequency masking, ratio masking, CHiME-2, robust ASR, joint training}
}

@article{10.1109/TASLP.2014.2375572,
author = {Su, Pei-Hao and Wu, Chuan-Hsun and Lee, Lin-Shan},
title = {A Recursive Dialogue Game for Personalized Computer-Aided Pronunciation Training},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2375572},
doi = {10.1109/TASLP.2014.2375572},
abstract = {Learning languages in addition to the native language is very important for all people in the globalized world today, and computer-aided pronunciation training (CAPT) is attractive since the software can be used anywhere at any time, and repeated as many times as desired. In this paper, we introduce the immersive interaction scenario offered by spoken dialogues to CAPT by proposing a recursive dialogue game to make CAPT personalized. A number of tree-structured sub-dialogues are linked sequentially and recursively as the script for the game. The system policy at each dialogue turn is to select in real-time along the dialogue the best training sentence for each specific individual learner within the dialogue script, considering the learner's learning status and the future possible dialogue paths in the script, such that the learner can have the scores for all pronunciation units considered reaching a predefined standard in a minimum number of turns. The purpose here is that those pronunciation units poorly produced by the specific learner can be offered with more practice opportunities in the future sentences along the dialogue, which enables the learner to improve the pronunciation without having to repeat the same training sentences many times. This makes the learning process for each learner completely personalized. The dialogue policy is modeled by Markov decision process (MDP) with high-dimensional continuous state space, and trained with fitted value iteration using a huge number of simulated learners. These simulated leaners have the behavior similar to real learners, and were generated from a corpus of real learner data. The experiments demonstrated very promising results and a real cloud-based system is also successfully implemented.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {127–141},
numpages = {15},
keywords = {dialogue game, computer-assisted language learning, computer-aided pronunciation training (CAPT), Markov decision process, reinforcement learning}
}

@article{10.1109/TASLP.2014.2367821,
author = {Decorsi\`{e}re, R\'{e}mi and S\o{}ndergaard, Peter L. and MacDonald, Ewen N. and Dau, Torsten},
title = {Inversion of Auditory Spectrograms, Traditional Spectrograms, and Other Envelope Representations},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2367821},
doi = {10.1109/TASLP.2014.2367821},
abstract = {Envelope representations such as the auditory or traditional spectrogram can be defined by the set of envelopes from the outputs of a filterbank. Common envelope extraction methods discard information regarding the fast fluctuations, or phase, of the signal. Thus, it is difficult to invert, or reconstruct a time-domain signal from, an arbitrary envelope representation. To address this problem, a general optimization approach in the time domain is proposed here, which iteratively minimizes the distance between a target envelope representation and that of a reconstructed time-domain signal. Two implementations of this framework are presented for auditory spectrograms, where the filterbank is based on the behavior of the basilar membrane and envelope extraction is modeled on the response of inner hair cells. One implementation is direct while the other is a two-stage approach that is computationally simpler. While both can accurately invert an auditory spectrogram, the two-stage approach performs better on time-domain metrics. The same framework is applied to traditional spectrograms based on the magnitude of the short-time Fourier transform. Inspired by human perception of loudness, a modification to the framework is proposed, which leads to a more accurate inversion of traditional spectrograms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {46–56},
numpages = {11},
keywords = {short-time Fourier transform, gradient methods, spectrogram inversion, auditory spectrogram}
}

@article{10.1109/TASLP.2014.2380352,
author = {Wang, Lin and Chen, Zhe and Yin, Fuliang},
title = {A Novel Hierarchical Decomposition Vector Quantization Method for High-Order LPC Parameters},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2380352},
doi = {10.1109/TASLP.2014.2380352},
abstract = {The paper investigates vector quantization coding of high-order (e.g., 20th-50th order) linear prediction coding (LPC) parameters, and proposes a novel hierarchical decomposition vector quantization method for a scalable speech coding framework with variable orders of LPC analysis. Instead of vector quantizing the whole group of LPC parameters in the linear spectral frequency (LSF) domain directly, the proposed method decomposes the high-order LPC model into several low-order (e.g., 10th-order) LPC models, and vector quantizes them in the LSF domain separately. For the decomposition, the high-order LPC model is converted into a group of reflection coefficients at first, and then the group is split into several subgroups and converted into multiple low-order LPC models. It is shown that the proposed method is naturally suitable for a scalable coding framework where the information of the decomposed low-order LPC models can be encoded into a multi-layered bitstream and can be combined in a progressive way to recover the high-order LPC information. Experiments in a scalable coding framework with variable LPC analysis orders (10-50) reveal that, compared to a direct vector quantization scheme, the proposed method can reduce the size of the codebook and the number of coding bits significantly, and can also efficiently reduce the computation cost.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {212–221},
numpages = {10},
keywords = {scalable coding, linear prediction coding (LPC), vector quantization, reflection coefficient, line spectral frequency}
}

@article{10.1109/TASLP.2014.2377575,
author = {Khoubrouy, Soudeh A. and Panahi, Issa M. S. and Hansen, John H. L.},
title = {Howling Detection in Hearing Aids Based on Generalized Teager-Kaiser Operator},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2377575},
doi = {10.1109/TASLP.2014.2377575},
abstract = {With the ongoing miniaturization in the hearing aid industry, acoustical coupling between the loudspeaker and the microphone(s) of the hearing aid causes a major problem to users. Howling is one of the most severe and annoying consequences of this acoustical coupling. This study presents a howling detection method using the Generalized Teager-Kaiser Operator (GTKO). Since the GTKO is both time and frequency sensitive, its resolution parameter must be assigned properly to ensure satisfactory performance of this operator in the frequency range of the input signal for the hearing aid. In order to cover the entire band of the input signal with appropriate resolution parameters, the input signal is decomposed into a filterbank (i.e., uniform and nonuniform filterbanks). GTKO is applied to the output of each band to detect the howling, and the resolution parameter of the GTKO block is selected depending on the central frequency of that particular band. Experimental results compare the performance of each proposed method with two known howling detection approaches, Peak-to-harmonic power ratio (PHPR) approach and a multiple-feature approach. The proposed method has high detection probability and short detection time. It is also shown that considering a hybrid algorithm which includes the PHPR approach with each of the proposed methods (i.e. combination of GTKO blocks with different types of filterbanks) results in lower false alarm probability.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {154–161},
numpages = {8},
keywords = {uniform filterbank, howling detection, non-uniform filterbank, acoustic feedback, hearing aid, Teager-Kaiser operator}
}

@article{10.1109/TASLP.2014.2360461,
author = {Yan, Su and Wan, Xiaojun},
title = {SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2360461},
doi = {10.1109/TASLP.2014.2360461},
abstract = {Extractive multi-document summarization systems usually rank sentences in a document set with some ranking strategy and then select a few highly ranked sentences into the summary. One of the most popular ranking algorithms is the graph-based ranking algorithm. In this paper, we investigate making use of semantic role information to enhance the graph-based ranking algorithm for multi-document summarization. We first parse the sentences and obtain the semantic roles, and then propose a novel SRRank algorithm and two extensions to make better use of the semantic role information. Our proposed algorithms can simultaneously rank the sentences, semantic roles and words in a heterogeneous ranking process. Experimental results on two DUC datasets demonstrate that our proposed algorithms significantly outperform a few baselines, and the semantic role information is validated to be very helpful for multidocument summarization.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2048–2058},
numpages = {11},
keywords = {multi-document summarization, semantic roles, graph-based ranking algorithm}
}

@article{10.1109/TASLP.2014.2348916,
author = {Percival, Graham and Tzanetakis, George},
title = {Streamlined Tempo Estimation Based on Autocorrelation and Cross-Correlation with Pulses},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2348916},
doi = {10.1109/TASLP.2014.2348916},
abstract = {Algorithms for musical tempo estimation have become increasingly complicated in recent years. These algorithms typically utilize two fundamental properties of musical rhythm: some features of the audio signal are self-similar at periods related to the underlying rhythmic structure, and rhythmic events tend to be spaced regularly in time. We present a streamlined tempo estimation method (stem) that distills ideas from previous work by reducing the number of steps, parameters, and modeling assumptions while retaining good accuracy. This method is designed for music with a constant or near-constant tempo. The proposed method either outperforms or has similar performance to many existing state-of-the-art algorithms. Self-similarity is captured through autocorrelation of the onset strength signal (OSS), and time regularity is captured through cross-correlation of the OSS with regularly spaced pulses. Our findings are supported by the most comprehensive evaluation of tempo estimation algorithms to date in terms of the number of datasets and tracks considered. During the process we have also corrected ground truth annotations for the datasets considered. All the data, the annotations, the evaluation code, and three different implementations (C++, Python, MATLAB) of the proposed algorithm are provided in order to support reproducibility.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1765–1776},
numpages = {12},
keywords = {rhythm analysis, music information retrieval, tempo induction, audio signal processing}
}

@article{10.1109/TASLP.2014.2354236,
author = {Krawczyk, Martin and Gerkmann, Timo},
title = {STFT Phase Reconstruction in Voiced Speech for an Improved Single-Channel Speech Enhancement},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2354236},
doi = {10.1109/TASLP.2014.2354236},
abstract = {The enhancement of speech which is corrupted by noise is commonly performed in the short-time discrete Fourier transform domain. In case only a single microphone signal is available, typically only the spectral amplitude is modified. However, it has recently been shown that an improved spectral phase can as well be utilized for speech enhancement, e.g., for phase-sensitive amplitude estimation. In this paper, we therefore present a method to reconstruct the spectral phase of voiced speech from only the fundamental frequency and the noisy observation. The importance of the spectral phase is highlighted and we elaborate on the reason why noise reduction can be achieved by modifications of the spectral phase. We show that, when the noisy phase is enhanced using the proposed phase reconstruction, instrumental measures predict an increase of speech quality over a range of signal to noise ratios, even without explicit amplitude enhancement.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1931–1940},
numpages = {10},
keywords = {speech enhancement, noise reduction, phase estimation, signal reconstruction}
}

@article{10.1109/TASLP.2014.2346314,
author = {Chivukula, Ravi K. and Reznik, Yuriy A. and Hu, Yanyan and Devarajan, Venkat and Jayendra-Lakshman, Mythreya},
title = {Fast Algorithms for Low-Delay TDAC Filterbanks in MPEG-4 AAC-ELD},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2346314},
doi = {10.1109/TASLP.2014.2346314},
abstract = {The MPEG committee has completed development of a new audio coding standard called "MPEG-4 advanced audio coding-enhanced low delay" (AAC-ELD). AAC-ELD uses low delay spectral band replication (LD-SBR) technology together with a low delay time domain alias cancellation (LD TDAC) filterbank in the encoder to achieve both high coding efficiency and low algorithmic delay. In this paper, we present fast algorithms for implementing LD-TDAC filterbanks in AAC-ELD. Two types of fast algorithms are presented. In the first, we map LD-TDAC analysis and synthesis filterbanks to modified discrete cosine transform (MDCT) and inverse modified discrete cosine transform (IMDCT), respectively. Since MDCT/IMDCT are already extensively used in AAC and they have many fast algorithms, this mapping not only provides a fast implementation but also allows a common implementation of the filterbanks in AAC Low Complexity (AAC-LC), AAC Low Delay (AAC-LD) and AAC-ELD codecs. In the second algorithm, we provide a mapping to discrete Cosine transform of type II. The mapping to DCT-II allows the merger of the matrix operations with the windowing stage that precedes or follows them. This further reduces the number of multiplications and leads to an algorithm with the lowest known arithmetic complexity. For filterbanks of lengths 1024 and 960, we also present a new fast factorization of 15-point DCT-II that requires only 14 irrational multiplications, 3 dyadic rational multiplications and 67 additions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1701–1712},
numpages = {12},
keywords = {AAC, MDCT, filterbanks, DCT, audio coding, low delay, time domain alias cancellation, MPEG, speech coding, fast algorithms, factorization}
}

@article{10.1109/TASLP.2014.2351131,
author = {Tachibana, Hideyuki and Ono, Nobutaka and Kameoka, Hirokazu and Sagayama, Shigeki},
title = {Harmonic/Percussive Sound Separation Based on Anisotropic Smoothness of Spectrograms},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2351131},
doi = {10.1109/TASLP.2014.2351131},
abstract = {This paper describes a method to separate a monaural music signal into harmonic components e.g., a guitar and percussive components, e.g., a snare drum. Separation of these two components is a useful preprocessing for many music information retrieval applications, and in addition, it can be used as a new kind of music equalizer in itself, which enables a music listener to adjust the ratio of the volume of the guitar and the drum freely by themselves. Because of these potential applications, there have been many attempts to develop such a technique, especially in the last decade. However, some of the state-of-the-art techniques have a drawback that they are based on costly operations, such as the multiplications of large-sized matrix, Monte Carlo method, etc., which may constitute barriers to the practical use on some small computers such as smart phones. In this paper, an efficient method that does not depend on these costly operations is described. In formulating the methods, the authors basically assumed only the "anisotropic smoothness" of music spectrogram, which can be one of the minimalistic model that reflects the natures of these instruments. To be specific, the authors just assumed that harmonic instruments are smooth in time, while the percussive instruments are smooth in frequency on a music spectrogram. In this paper, on the basis of the assumption, source separation methods are formulated as optimization problems that optimize the "anisotropic smoothness" under some conditions. Because of the simplicity of the model, the derived algorithms are quite simple. Experimental results show that the methods were effective compared to a state-of-the-art technique, and the computation time was much shorter than an existing method; specifically, it can process a three-minute song in around 4-20 seconds on a laptop PC.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2059–2073},
numpages = {15},
keywords = {percussion, music signal processing, harmonic instruments, audio source separation}
}

@article{10.1109/TASLP.2014.2365971,
author = {FanChiang, Yi and Wei, Cheng-Wen and Meng, Yi-Le and Lin, Yu-Wen and Jou, Shyh-Jye and Chang, Tian-Sheuan},
title = {Correction to "Low Complexity Formant Estimation Adaptive Feedback Cancellation for Hearing Aids Using Pitch Based Processing"},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2365971},
doi = {10.1109/TASLP.2014.2365971},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2256},
numpages = {1}
}

@article{10.1109/TASLP.2014.2346313,
author = {Xue, Shaofei and Abdel-Hamid, Ossama and Jiang, Hui and Dai, Lirong and Liu, Qingfeng},
title = {Fast Adaptation of Deep Neural Network Based on Discriminant Codes for Speech Recognition},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2346313},
doi = {10.1109/TASLP.2014.2346313},
abstract = {Fast adaptation of deep neural networks (DNN) is an important research topic in deep learning. In this paper, we have proposed a general adaptation scheme for DNN based on discriminant condition codes, which are directly fed to various layers of a pre-trained DNN through a new set of connection weights. Moreover, we present several training methods to learn connection weights from training data as well as the corresponding adaptation methods to learn new condition code from adaptation data for each new test condition. In this work, the fast adaptation scheme is applied to supervised speaker adaptation in speech recognition based on either frame-level cross-entropy or sequence-level maximum mutual information training criterion. We have proposed three different ways to apply this adaptation scheme based on the so-called speaker codes: i) Nonlinear feature normalization in feature space; ii) Direct model adaptation of DNN based on speaker codes; iii) Joint speaker adaptive training with speaker codes. We have evaluated the proposed adaptation methods in two standard speech recognition tasks, namely TIMIT phone recognition and large vocabulary speech recognition in the Switchboard task. Experimental results have shown that all three methods are quite effective to adapt large DNN models using only a small amount of adaptation data. For example, the Switchboard results have shown that the proposed speaker-code-based adaptation methods may achieve up to 8-10% relative error reduction using only a few dozens of adaptation utterances per speaker. Finally, we have achieved very good performance in Switchboard (12.1% in WER) after speaker adaptation using sequence training criterion, which is very close to the best performance reported in this task ("Deep convolutional neural networks for LVCSR," T. N. Sainath et al., Proc. IEEE Acoust., Speech, Signal Process., 2013).},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1713–1725},
numpages = {13},
keywords = {speaker code, cross entropy (CE), maximum mutual information (MMI), deep neural network (DNN), condition code, fast adaptation}
}

@article{10.1109/TASLP.2014.2352495,
author = {D'Angelo, Stefano and V\"{a}lim\"{a}ki, Vesa},
title = {Generalized Moog Ladder Filter: Part I-Linear Analysis and Parameterization},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2352495},
doi = {10.1109/TASLP.2014.2352495},
abstract = {The Moog ladder filter, which consists of four cascaded first-order ladder stages in a feedback loop, falls within the class of devices that have attracted greatest interest in virtual analog research. On one hand, this work confirms that the presence of exactly four stages in the original analog circuit is motivated by specific filter control issues and, on the other, that such a limitation can be overcome in the digital domain with relative ease. First, a continuous-time large-signal model is defined for a version of the circuit that is generalized to an arbitrary number of ladder stages. Then, the linear behavior of the filter around its natural operating point and the effect of control parameters on the resulting frequency response are studied in depth, to obtain exact analytical expressions for the position of poles in the transfer function and for the dc gain of the filter, as well as a parameterization strategy that is consistent for any number of ladder stages. A previously-introduced linear digital model of the device suggested by Smith is eventually generalized based on these general results, which remain, however, relevant and similarly applicable to other discretizations of the filter. The proposed model faithfully reproduces the linear behavior of the generalized device while providing sensible parametric control for any number of ladder stages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1825–1832},
numpages = {8},
keywords = {IIR filters, resonator filters, music, acoustic signal processing, circuit simulation}
}

@article{10.1109/TASLP.2014.2352456,
author = {Zakharov, Yuriy and Nascimento, V\'{\i}tor H.},
title = {Sliding-Window RLS Low-Cost Implementation of Proportionate Affine Projection Algorithms},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2352456},
doi = {10.1109/TASLP.2014.2352456},
abstract = {This paper addresses adaptive filtering for sparse identification. Proportionate affine projection algorithms (PAPAs) are known to be efficient techniques for this purpose. We show that the PAPA performance may improve with an increase in the projection order M (for example, such as M = 512 ), which, however, also results in an increased complexity; the complexity is in general O(M2N) or at least O(M N) operations per sample, where N is the filter length. We show that PAPAs are equivalent to specific sliding-window recursive least squares (SRLS) adaptive algorithms with time-varying and tap-varying diagonal loading (SRLS-VDLs). We then propose an approximation to the SRLS-VDLs based on dichotomous coordinate descent (DCD) iterations with a complexity of O(NuN), which does not depend on M; it depends on the number of DCD iterations Nu, which as we show can be significantly smaller than M, thus allowing a low-complexity implementation of PAPA adaptive filters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1815–1824},
numpages = {10},
keywords = {adaptive filter, sliding window, DCD, dichotomous coordinate descent, diagonal loading, PAPA, sparse identification, RLS, affine projection}
}

@article{10.1109/TASLP.2014.2357677,
author = {Wang, Qi and Woo, W. L. and Dlay, S. S.},
title = {Informed Single-Channel Speech Separation Using HMM-GMM User-Generated Exemplar Source},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2357677},
doi = {10.1109/TASLP.2014.2357677},
abstract = {We present a new approach for solving the single channel speech separation with the aid of an user-generated exemplar source that is recorded from a microphone. Our method deviates from the conventional model-based methods, which highly rely on speaker dependent training data. We readdress the problem by offering a new approach based on utterance dependent patterns extracted from the user-generated exemplar source. Our proposed approach is less restrictive, and does not require speaker dependent information and yet exceeds the performance of conventional model-based separation methods in separating male and male speech mixtures. We combine general speaker-independent (SI) features with specifically generated utterance-dependent (UD) features in a joint probability model. The UD features are initially extracted from the user-generated exemplar source and represented as statistical estimates. These estimates are calibrated based on information extracted from the mixture source to statistically represent the target source. The UD probability model is subsequently generated to target problems of ambiguity and to offer better cues for separation. The proposed algorithm is tested and compared with recent method using the GRID database and the Mocha-TIMIT database.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2087–2100},
numpages = {14},
keywords = {exemplar assistance, Gaussian mixture model (GMM), single-channel source separation (SCSS), speaker-assisted source separation, factorial hidden Markov model(FHMM), concurrent pitch tracking, informed source separation (ISS)}
}

@article{10.1109/TASLP.2014.2353991,
author = {Chen, Ling-Hui and Ling, Zhen-Hua and Liu, Li-Juan and Dai, Li-Rong},
title = {Voice Conversion Using Deep Neural Networks with Layer-Wise Generative Training},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2353991},
doi = {10.1109/TASLP.2014.2353991},
abstract = {This paper presents a new spectral envelope conversion method using deep neural networks (DNNs). The conventional joint density Gaussian mixture model (JDGMM) based spectral conversion methods perform stably and effectively. However, the speech generated by these methods suffer severe quality degradation due to the following two factors: 1) inadequacy of JDGMM in modeling the distribution of spectral features as well as the non-linear mapping relationship between the source and target speakers, 2) spectral detail loss caused by the use of high-level spectral features such as mel-cepstra. Previously, we have proposed to use the mixture of restricted Boltzmann machines (MoRBM) and the mixture of Gaussian bidirectional associative memories (MoGBAM) to cope with these problems. In this paper, we propose to use a DNN to construct a global non-linear mapping relationship between the spectral envelopes of two speakers. The proposed DNN is generatively trained by cascading two RBMs, which model the distributions of spectral envelopes of source and target speakers respectively, using a Bernoulli BAM (BBAM). Therefore, the proposed training method takes the advantage of the strong modeling ability of RBMs in modeling the distribution of spectral envelopes and the superiority of BAMs in deriving the conditional distributions for conversion. Careful comparisons and analysis among the proposed method and some conventional methods are presented in this paper. The subjective results show that the proposed method can significantly improve the performance in terms of both similarity and naturalness compared to conventional methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1859–1872},
numpages = {14},
keywords = {voice conversion, Gaussian mixture model, bidirectional associative memory, spectral envelope conversion, deep neural network, restricted Boltzmann machine}
}

@article{10.1109/TASLP.2014.2354241,
author = {R\"{a}m\"{o}, Jussi and V\"{a}lim\"{a}ki, Vesa and Bank, Bal\'{a}zs},
title = {High-Precision Parallel Graphic Equalizer},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2354241},
doi = {10.1109/TASLP.2014.2354241},
abstract = {This paper proposes a high-precision graphic equalizer based on second-order parallel filters. Previous graphic equalizers suffer from interaction between adjacent band filters, especially at high gain values, which can lead to substantial errors in the magnitude response. The fixed-pole design of the proposed parallel graphic equalizer avoids this problem, since the parallel second-order filters are optimized jointly. When the number of pole frequencies is twice the number of command points of the graphic equalizer, the proposed non-iterative design matches the target curve with high precision. In the three example cases presented in this paper, the proposed parallel equalizer clearly outperforms other non-iterative graphic equalizer designs, and its maximum global error is as low as 0.00-0.75 dB when compared to the target curve. While the proposed design has superior accuracy, the number of operations in the filter structure is increased only by 23% when compared to the second-order Regalia-Mitra structure. The parallel structure also enables the utilization of parallel computing hardware, which can nowadays easily outperform the traditional serial processing. The proposed graphic equalizer can be widely used in audio signal processing applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1894–1904},
numpages = {11},
keywords = {audio systems, digital signal processing, infinite impulse response (IIR) filters, acoustic signal processing, equalizers}
}

@article{10.1109/TASLP.2014.2362006,
author = {Su, Li and Lin, Hsin-Ming and Yang, Yi-Hsuan},
title = {Sparse Modeling of Magnitude and Phase-Derived Spectra for Playing Technique Classification},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2362006},
doi = {10.1109/TASLP.2014.2362006},
abstract = {Computational modeling of musical timbre is important for a variety of music information retrieval applications. While considerable progress has been made to recognize musical genres and instruments, relatively little attention has been paid to modeling playing techniques, which affect timbre in more subtle ways. In this paper, we contribute to this area of research by systematically evaluating various audio features and processing methods for multi-class playing technique classification, considering up to nine distinct playing techniques of bowed string instruments. Specifically, a collection of 6,759 chamber-recorded single notes of four bowed string instruments and a collection of 33 real-world solo violin recordings are used in the evaluation. Our evaluation shows that using sparse features extracted from the magnitude spectra and phase derivatives including group delay function (GDF) and instantaneous frequency deviation (IFD) leads to significantly better performance than using a combination of state-of-the-art temporal, spectral, cepstral and harmonic feature descriptors. For playing technique classification of violin singe notes, the former approach attains 0.915 macro-average F-score under a tenfold cross validation setting, while the latter only attains 0.835. Moreover, sparse modeling of magnitude and phase-derived spectra also performs well for single-note joint instrument-technique classification (F-score 0.770) and for playing technique classification of real-world violin solos (F-score 0.547). We find that phase information is particularly important in discriminating playing techniques with subtle differences, such as playing with different bowing positions (i.e., normal, sul tasto, and sul ponticello). A systematic investigation of the effect of parameters such as window sizes, hop factors, window types for phase-derived features is also reported to provide more insights.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2122–2132},
numpages = {11},
keywords = {instantaneous frequency deviation, sparse coding, phase, group delay function, playing technique classification}
}

@article{10.1109/TASLP.2014.2363407,
author = {Thiergart, Oliver and Taseska, Maja and Habets, Emanu\"{e}l A. P.},
title = {An Informed Parametric Spatial Filter Based on Instantaneous Direction-of-Arrival Estimates},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2363407},
doi = {10.1109/TASLP.2014.2363407},
abstract = {Extracting desired source signals in noisy and reverberant environments is required in many hands-free communication systems. In practical situations, where the position and number of active sources may be unknown and time-varying, conventional implementations of spatial filters do not provide sufficiently good performance. Recently, informed spatial filters have been introduced that incorporate almost instantaneous parametric information on the sound field, thereby enabling adaptation to new acoustic conditions and moving sources. In this contribution, we propose a spatial filter which generalizes the recently proposed informed linearly constrained minimum variance filter and informed minimum mean square error filter. The proposed filter uses multiple direction-of-arrival estimates and second-order statistics of the noise and diffuse sound. To determine those statistics, an optimal diffuse power estimator is proposed that outperforms state-of-the-art estimators. Extensive performance evaluation demonstrates the effectiveness of the proposed filter in dynamic acoustic conditions. For this purpose, we have considered a challenging scenario which consists of quickly moving sound sources during double-talk. The performance of the proposed spatial filter was evaluated in terms of objective measures including segmental signal-to-reverberation ratio and log spectral distance, and by means of a listening test confirming the objective results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2182–2196},
numpages = {15},
keywords = {microphone array processing, dereverberation, interference reduction, optimal beamforming}
}

@article{10.1109/TASLP.2014.2370434,
author = {Deng, Li},
title = {Farewell Editorial: Keeping up the Momentum of Innovations},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2370434},
doi = {10.1109/TASLP.2014.2370434},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1687},
numpages = {1}
}

@article{10.1109/TASLP.2014.2355821,
author = {Venturini, A. and Z\~{a}o, L. and Coelho, R.},
title = {On Speech Features Fusion, α-Integration Gaussian Modeling and Multi-Style Training for Noise Robust Speaker Classification},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2355821},
doi = {10.1109/TASLP.2014.2355821},
abstract = {This paper investigates the fusion of Mel-frequency cepstral coefficients (MFCC) and statistical pH features to improve the performance of speaker verification (SV) in non-stationary noise conditions. The α-integrated Gaussian Mixture Model (α-GMM) classifier is adopted for speaker modeling. Two different approaches are applied to reduce the effects of noise corruption in the SV task: speech enhancement and multi-style training (MT). The spectral subtraction with minimum statistics (MS/SS) and the optimally-modified log-spectral amplitude with improved minima controlled recursive averaging (IMCRA/OMLSA) are examined for the speech enhancement procedure. The MT techniques are based on colored (Colored-MT), white (White-MT) and narrow-band (Narrow-MT) noises. Six real non-stationary noises, collected from different acoustic sources, are used to corrupt the TIMIT speech database in four different signal-to-noise ratios (SNR). The index of non-stationarity (INS) is chosen for the stationarity tests of the acoustic noises. Complementary SV experiments are conducted in realistic noisy conditions using the MIT database. The results show that the best SV accuracy was obtained with the MFCC + pH features fusion, the MS/SS and the Colored-MT.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1951–1964},
numpages = {14},
keywords = {features fusion, non-stationary acoustic noise, hurst exponent, speech enhancement, speaker verification, multi-style training, α-GMM}
}

@article{10.1109/TASLP.2014.2352451,
author = {Khanagha, Vahid and Daoudi, Khalid and Yahia, Hussein M.},
title = {Detection of Glottal Closure Instants Based on the Microcanonical Multiscale Formalism},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2352451},
doi = {10.1109/TASLP.2014.2352451},
abstract = {This paper presents a novel algorithm for automatic detection of Glottal Closure Instants (GCI) from the speech signal. Our approach is based on a novel multiscale method that relies on precise estimation of a multiscale parameter at each time instant in the signal domain. This parameter quantifies the degree of signal singularity at each sample from a multi-scale point of view and thus its value can be used to classify signal samples accordingly. We use this property to develop a simple algorithm for detection of GCIs and we show that for the case of clean speech, our algorithm performs almost as well as a recent state-of-the-art method. Next, by performing a comprehensive comparison in presence of 14 different types of noises, we show that our method is more accurate (particularly for very low SNRs). Our method has lower computational times compared to others and does not rely on an estimate of pitch period or any critical choice of parameters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1941–1950},
numpages = {10},
keywords = {nonlinear speech analysis, multiscale signal processing, detection of glottal closure instant}
}

@article{10.1109/TASLP.2014.2349856,
author = {Barkefors, Annea and Sternad, Mikael and Br\"{a}nnmark, Lars-Johan},
title = {Design and Analysis of Linear Quadratic Gaussian Feedforward Controllers for Active Noise Control},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2349856},
doi = {10.1109/TASLP.2014.2349856},
abstract = {A method for sound field control applied to active noise control is presented and evaluated. The method uses Linear Quadratic Gaussian (LQG) feedforward control to find a Minimal Mean Square Error (MMSE)-optimal linear sound field controller under a causality constraint. It is obtained by solving a polynomial matrix spectral factorization and a linear (Diophantine) polynomial matrix equation. An important component in the design is the control signal penalty term of the criterion. Its use and influence is here discussed and evaluated using measured room impulse responses. The results indicate that the use of a relatively simple, frequency-weighted penalty on individual control signals provides most of the benefits obtainable by the considered more advanced alternative. We also introduce and illustrate several tools for performance analysis. An analytical expression for the attainable performance clearly reveals the performance loss generated by having to use a causal controller instead of the ideal noncausal controller. This loss is largest at low frequencies. Furthermore, we introduce a measure of the reproducibility of the target noise sound field with given control loudspeaker setups and room transfer functions. It describes how well a controller that uses an input subspace of dimension equal to the effective rank of the system is able to reproduce a target sound field. This performance measure can e.g. be used to support the selection of good combinations of placements of control loudspeakers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1777–1791},
numpages = {15},
keywords = {sound field reproducibility, sound field control, linear quadratic control, active noise reduction, feedforward control, effective rank, causality constraints}
}

@article{10.1109/TASLP.2014.2365701,
author = {Traa, Johannes and Smaragdis, Paris},
title = {Multichannel Source Separation and Tracking with RANSAC and Directional Statistics},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2365701},
doi = {10.1109/TASLP.2014.2365701},
abstract = {We describe multichannel blind source separation and tracking algorithms based on clustering wrapped interchannel phase difference (IPD) features. We pose the clustering problem as one of multimodal circular-linear regression and present its probabilistic formulation. Phase wrapping due to spatial aliasing is explicitly incorporated by modeling the IPD features as circular variables. We present two methods based on Expectation-Maximization (EM) and a sequential variant of RANdom SAmple Consensus (RANSAC). We show that their strengths can be combined by using RANSAC to initialize EM. The IPD clustering algorithm is applied to separate stationary speakers from a multi-channel mixture. We then extend it to the case of moving speakers by tracking their directions-of-arrival with the Factorial Wrapped Kalman Filter (FWKF) using RANSAC as a data preprocessor. Experimental results demonstrate that the proposed methods perform well in the presence of reverberant babble noise and spatial aliasing. The FWKF successfully tracks and separates moving speakers with separation quality comparable to that for stationary speakers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2233–2243},
numpages = {11},
keywords = {blind source separation (BSS), interchannel phase difference (IPD), directional statistics, wrapped Kalman filter}
}

@article{10.1109/TASLP.2014.2363790,
author = {Otsuka, Takuma and Ishiguro, Katsuhiko and Yoshioka, Takuya and Sawada, Hiroshi and Okuno, Hiroshi G.},
title = {Multichannel Sound Source Dereverberation and Separation for Arbitrary Number of Sources Based on Bayesian Nonparametrics},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2363790},
doi = {10.1109/TASLP.2014.2363790},
abstract = {Multichannel signal processing using a microphone array provides fundamental functions for coping with multisource situations, such as sound source localization and separation, that are needed to extract the auditory information for each source. Auditory uncertainties about the degree of reverberation and the number of sources are known to degrade performance or limit the practical application of microphone array processing. Such uncertainties must therefore be overcome to realize general and robust microphone array processing. These uncertainty issues have been partly addressed--existing methods focus on either source number uncertainty or the reverberation issue, where joint separation and dereverberation has been achieved only for the overdetermined conditions. This paper presents an all-round method that achieves source separation and dereverberation for an arbitrary number of sources including underdetermined conditions. Our method uses Bayesian nonparametrics that realize an infinitely extensible modeling flexibility so as to bypass the model selection in the separation and dereverberation problem, which is caused by the source number uncertainty. Evaluation using a dereverberation and separation task with various numbers of sources including underdetermined conditions demonstrates that (1) our method is applicable to the separation and dereverberation of underdetermined mixtures, and that (2) the source extraction performance is comparable to that of a state-of-the-art method suitable only for overdetermined conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2218–2232},
numpages = {15},
keywords = {blind dereverberation, Bayesian nonparametrics, underdetermined mixtures, microphone array processing, blind source separation, Markov chain Monte Carlo method}
}

@article{10.1109/TASLP.2014.2355772,
author = {Maezawa, Akira and Itoyama, Katsutoshi and Yoshii, Kazuyoshi and Okuno, Hiroshi G.},
title = {Nonparametric Bayesian Dereverberation of Power Spectrograms Based on Infinite-Order Autoregressive Processes},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2355772},
doi = {10.1109/TASLP.2014.2355772},
abstract = {This paper describes a monaural audio dereverberation method that operates in the power spectrogram domain. The method is robust to different kinds of source signals such as speech or music. Moreover, it requires little manual intervention, including the complexity of room acoustics. The method is based on a non-conjugate Bayesian model of the power spectrogram. It extends the idea of multi-channel linear prediction to the power spectrogram domain, and formulates a model of reverberation as a non-negative, infinite-order autoregressive process. To this end, the power spectrogram is interpreted as a histogram count data, which allows a nonparametric Bayesian model to be used as the prior for the autoregressive process, allowing the effective number of active components to grow, without bound, with the complexity of data. In order to determine the marginal posterior distribution, a convergent algorithm, inspired by the variational Bayes method, is formulated. It employs the minorization-maximization technique to arrive at an iterative, convergent algorithm that approximates the marginal posterior distribution. Both objective and subjective evaluations show advantage over other methods based on the power spectrum. We also apply the method to a music information retrieval task and demonstrate its effectiveness.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1918–1930},
numpages = {13},
keywords = {nonparameteric Bayes, dereverberation, minorization maximization}
}

@article{10.1109/TASLP.2014.2363414,
author = {Mu, Yongsheng and Ji, Peifeng and Ji, Wei and Wu, Ming and Yang, Jun},
title = {Modeling and Compensation for the Distortion of Parametric Loudspeakers Using a One-Dimension Volterra Filter},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2363414},
doi = {10.1109/TASLP.2014.2363414},
abstract = {Recently, the general Volterra filter (VF) has been adopted for the modeling of parametric loudspeakers. However, the computation complexity of the VF is too high for real-time implementation. In this paper, a one-dimension Volterra filter (ODVF) with much lower complexity is introduced to model and compensate for the nonlinearity of parametric loudspeakers. A theoretical framework for ODVF model identification is established and a method of measuring the ODVF kernels using the exponential swept-sine signal is provided. The validity of modeling the nonlinearity of the parametric loudspeaker using the ODVF is verified theoretically and experimentally. Based on the established ODVF model, an inverse filter is designed to compensate for the 2nd harmonic distortion of the parametric loudspeakers. To further reduce the 3rd harmonic distortion, an improved compensation method is also proposed. Experimental results show that the performance of the ODVF-based compensation is comparable to that of the Volterra-filter based compensation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2169–2181},
numpages = {13},
keywords = {pth-order inverse, one-dimension Volterra filter (ODVF), parametric loudspeaker, Volterra filter (VF)}
}

@article{10.1109/TASLP.2014.2351133,
author = {Tourbabin, Vladimir and Rafaely, Boaz},
title = {Theoretical Framework for the Optimization of Microphone Array Configuration for Humanoid Robot Audition},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2351133},
doi = {10.1109/TASLP.2014.2351133},
abstract = {An important aspect of a humanoid robot is audition. Previous work has presented robot systems capable of sound localization and source segregation based on microphone arrays with various configurations. However, no theoretical framework for the design of these arrays has been presented. In the current paper, a design framework is proposed based on a novel array quality measure. The measure is based on the effective rank of a matrix composed of the generalized head related transfer functions (GHRTFs) that account for microphone positions other than the ears. The measure is shown to be theoretically related to standard array performance measures such as beamforming robustness and DOA estimation accuracy. Then, the measure is applied to produce sample designs of microphone arrays. Their performance is investigated numerically, verifying the advantages of array design based on the proposed theoretical framework.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1803–1814},
numpages = {12},
keywords = {robot audition, generalized HRTF, microphone array, DOA estimation, beamforming}
}

@article{10.1109/TASLP.2014.2352154,
author = {Liu, Gang and Hansen, John H. L.},
title = {An Investigation into Back-End Advancements for Speaker Recognition in Multi-Session and Noisy Enrollment Scenarios},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2352154},
doi = {10.1109/TASLP.2014.2352154},
abstract = {This study aims to explore the case of robust speaker recognition with multi-session enrollments and noise, with an emphasis on optimal organization and utilization of speaker information presented in the enrollment and development data. This study has two core objectives. First, we investigate more robust back-ends to address noisy multi-session enrollment data for speaker recognition. This task is achieved by proposing novel back-end algorithms. Second, we construct a highly discriminative speaker verification framework. This task is achieved through intrinsic and extrinsic back-end algorithm modification, resulting in complementary sub-systems. Evaluation of the proposed framework is performed on the NIST SRE2012 corpus. Results not only confirm individual sub-system advancements over an established baseline, the final grand fusion solution also represents a comprehensive overall advancement for the NIST SRE2012 core tasks. Compared with state-of-the-art SID systems on the NIST SRE2012, the novel parts of this study are: 1) exploring a more diverse set of solutions for low-dimensional i-Vector based modeling; and 2) diversifying the information configuration before modeling. All these two parts work together, resulting in very competitive performance with reasonable computational cost.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1978–1992},
numpages = {15},
keywords = {GCDS, speaker recognition, universal background support, classification algorithms, PLDA}
}

@article{10.1109/TASLP.2014.2352453,
author = {Yang, Na and Ba, He and Cai, Weiyang and Demirkol, Ilker and Heinzelman, Wendi},
title = {BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2352453},
doi = {10.1109/TASLP.2014.2352453},
abstract = {Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0 value among several F0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1833–1848},
numpages = {16},
keywords = {noise resilience, viterbi algorithm, harmonics, fundamental frequency detection, cepstrum}
}

@article{10.1109/TASLP.2014.2352935,
author = {Wang, Yuxuan and Narayanan, Arun and Wang, DeLiang},
title = {On Training Targets for Supervised Speech Separation},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2352935},
doi = {10.1109/TASLP.2014.2352935},
abstract = {Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1849–1858},
numpages = {10},
keywords = {speech separation, deep neural networks, supervised learning, training targets}
}

@article{10.1109/TASLP.2014.2354242,
author = {Rafii, Zafar and Duan, Zhiyao and Pardo, Bryan},
title = {Combining Rhythm-Based and Pitch-Based Methods for Background and Melody Separation},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2354242},
doi = {10.1109/TASLP.2014.2354242},
abstract = {Musical works are often composed of two characteristic components: the background (typically the musical accompaniment), which generally exhibits a strong rhythmic structure with distinctive repeating time elements, and the melody (typically the singing voice or a solo instrument), which generally exhibits a strong harmonic structure with a distinctive predominant pitch contour. Drawing from findings in cognitive psychology, we propose to investigate the simple combination of two dedicated approaches for separating those two components: a rhythm-based method that focuses on extracting the background via a rhythmic mask derived from identifying the repeating time elements in the mixture and a pitch-based method that focuses on extracting the melody via a harmonic mask derived from identifying the predominant pitch contour in the mixture. Evaluation on a data set of song clips showed that combining such two contrasting yet complementary methods can help to improve separation performance--from the point of view of both components--compared with using only one of those methods, and also compared with two other state-of-the-art approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1884–1893},
numpages = {10},
keywords = {background, pitch, separation, melody, rhythm}
}

@article{10.1109/TASLP.2014.2348913,
author = {Wachowski, Neil and Azimi-Sadjadi, Mahmood R.},
title = {Detection and Classification of Nonstationary Transient Signals Using Sparse Approximations and Bayesian Networks},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2348913},
doi = {10.1109/TASLP.2014.2348913},
abstract = {This paper considers sequential detection and classification of multiple transient signals from vector observations corrupted with additive noise and multiple types of structured interference. Sparse approximations of observations are found to facilitate computation of the likelihood of each signal model without relying on restrictive assumptions concerning the distribution of observations. Robustness to interference may be incorporated by virtue of the inherent separation capabilities of sparse coding. Each signal model is characterized by a Bayesian Network, which captures the temporal dependency structure among coefficients in successive sparse approximations under the associated hypothesis. Generalized likelihood ratios tests may then be used to perform signal detection and classification during quiescent periods, and quiescent detection whenever a signal is present. The results of applying the proposed method to a national park soundscape analysis problem demonstrate its practical utility for detecting and classifying real acoustical sources present in complex sonic environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1750–1764},
numpages = {15},
keywords = {sparse representations, signal classification, multivariate analysis, transient detection}
}

@article{10.1109/TASLP.2014.2341913,
author = {Van Mourik, Jelle and Murphy, Damian},
title = {Explicit Higher-Order FDTD Schemes for 3D Room Acoustic Simulation},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2341913},
doi = {10.1109/TASLP.2014.2341913},
abstract = {The Finite Difference Time Domain method is gaining popularity as a means to simulate and solve room acoustical problems. In this paper, a new set of stencils is defined that approximate the wave equation with a high degree of accuracy and lower dispersion error. Compared to the previously presented optimal scheme, the Interpolated Wideband scheme, our schemes are computationally less demanding and more practical to implement. They use at least 8 times less memory for the same audio rate and are an order of magnitude faster, although the former has a higher valid bandwidth. Despite their larger computational expense per node update, it is shown that our schemes on the whole use less memory and computation time than the Standard Rectilinear stencil, particularly when GPU implementations are employed. Lastly, a new way of visualizing and comparing valid bandwidth is recommended.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2003–2011},
numpages = {9},
keywords = {wave propagation, finite difference time domain (FDTD), numerical methods, acoustics}
}

@article{10.1109/TASLP.2014.2346315,
author = {Yella, Sree Harsha and Bourlard, Herv\'{e}},
title = {Overlapping Speech Detection Using Long-Term Conversational Features for Speaker Diarization in Meeting Room Conversations},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2346315},
doi = {10.1109/TASLP.2014.2346315},
abstract = {Overlapping speech has been identified as one of the main sources of errors in diarization of meeting room conversations. Therefore, overlap detection has become an important step prior to speaker diarization. Studies on conversational analysis have shown that overlapping speech is more likely to occur at specific parts of a conversation. They have also shown that overlap occurrence is correlated with various conversational features such as speech, silence patterns and speaker turn changes. We use features capturing this higher level information from structure of a conversation such as silence and speaker change statistics to improve acoustic feature based classifier of overlapping and single-speaker speech classes. The silence and speaker change statistics are computed over a long-term window (around 3-4 seconds) and are used to predict the probability of overlap in the window. These estimates are then incorporated into a acoustic feature based classifier as prior probabilities of the classes. Experiments conducted on three corpora (AMI, NIST-RT and ICSI) have shown that the proposed method improves the performance of acoustic feature-based overlap detector on all the corpora. They also reveal that the model based on long-term conversational features used to estimate probability of overlap which is learned from AMI corpus generalizes to meetings from other corpora (NIST-RT and ICSI). Moreover, experiments on ICSI corpus reveal that the proposed method also improves laughter overlap detection. Consequently, applying overlap handling techniques to speaker diarization using the detected overlap results in reduction of diarization error rate (DER) on all the three corpora.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1688–1700},
numpages = {13},
keywords = {simultaneous speakers, spontaneous conversations, meeting room recordings, speaker diarization, spontaneous overlapping speech}
}

@article{10.1109/TASLP.2014.2361022,
author = {Erro, Daniel and Zorila, Tudor-Catalin and Stylianou, Yannis},
title = {Enhancing the Intelligibility of Statistically Generated Synthetic Speech by Means of Noise-Independent Modifications},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2361022},
doi = {10.1109/TASLP.2014.2361022},
abstract = {When speaking devices such as smartphones, tablet-PCs, or GPS systems are used in noisy outdoor environments, the intelligibility of speech significantly drops. This is even more pronounced when synthetic speech is used. This article describes how a statistical parametric speech synthesis system trained on an ordinary synthesis database can be designed to generate highly intelligible speech, even at very low signal-to-noise ratios. By using a simple and flexible vocoder based on a full-band harmonic model, the proposed system applies deterministic noise-independent modifications at several levels: speaking rate, average fundamental frequency level and range, energy contour over time, formant sharpness, and intensity of specific spectral bands. The degree of intelligibility achieved by the system has been evaluated by means of a large-scale subjective test, the results of which show that the suggested approach clearly outperforms a reference state-of-the-art TTS system and also unmodified natural speech in some conditions. In comparison with alternative systems evaluated in the same framework, the proposed one exhibits the best performance in the scenarios with lowest signal-to-noise ratio. Finally, the impact of the suggested modifications on naturalness, quality and similarity to the original natural voice is quantified by means of a subjective test.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2101–2111},
numpages = {11},
keywords = {harmonic model, speech enhancement, voice transformation, speech intelligibility in noise, statistical parametric speech synthesis}
}

@article{10.1109/TASLP.2014.2362009,
author = {Mohammadi, Amir and Sarfjoo, Seyyed Saeed and Demiro\u{g}lu, Cenk},
title = {Eigenvoice Speaker Adaptation with Minimal Data for Statistical Speech Synthesis Systems Using a MAP Approach and Nearest-Neighbors},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2362009},
doi = {10.1109/TASLP.2014.2362009},
abstract = {Statistical speech synthesis (SSS) systems have the ability to adapt to a target speaker with a couple of minutes of adaptation data. Developing adaptation algorithms to further reduce the number of adaptation utterances to a few seconds of data can have substantial effect on the deployment of the technology in real-life applications such as consumer electronics devices. The traditional way to achieve such rapid adaptation is the eigenvoice technique which works well in speech recognition but known to generate perceptual artifacts in statistical speech synthesis. Here, we propose three methods to alleviate the quality problems of the baseline eigenvoice adaptation algorithm while allowing speaker adaptation with minimal data. Our first method is based on using a Bayesian eigenvoice approach for constraining the adaptation algorithm to move in realistic directions in the speaker space to reduce artifacts. Our second method is based on finding pretrained reference speakers that are close to the target speaker and utilizing only those reference speaker models in a second eigenvoice adaptation iteration. Both techniques performed significantly better than the baseline eigenvoice method in the objective tests. Similarly, they both improved the speech quality in subjective tests compared to the baseline eigenvoice method. In the third method, tandem use of the proposed eigenvoice method with a state-of-the-art linear regression based adaptation technique is found to improve adaptation of excitation features.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2146–2157},
numpages = {12},
keywords = {nearest neighbor, eigenvoice adaptation, speaker adaptation, cluster adaptive training, statistical speech synthesis}
}

@article{10.1109/TASLP.2014.2364130,
author = {Li, Weifeng and Wang, Longbiao and Zhou, Yicong and Dines, John and Magimai.-Doss, Mathew and Bourlard, Herv\'{e} and Liao, Qingmin},
title = {Feature Mapping of Multiple Beamformed Sources for Robust Overlapping Speech Recognition Using a Microphone Array},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2364130},
doi = {10.1109/TASLP.2014.2364130},
abstract = {This paper introduces a nonlinear vector-based feature mapping approach to extract robust features for automatic speech recognition (ASR) of overlapping speech using a microphone array. We explore different configurations and additional sources of information to improve the effectiveness of the feature mapping. First, we investigate the full-vector based mapping of different sources in a log mel-filterbank energy (log MFBE) domain, and demonstrate that retraining the acoustic model using the generated training data can help improve the recognition performance. Then we investigate the feature mapping between different domains. Finally in order to improve the qualities of the mapping inputs we propose a nonlinear mapping of the features from multiple beamformed sources, which are directed at the target and interfering speakers, respectively. We demonstrate the effectiveness of the proposed approach through extensive evaluations on the MONC corpus, which includes non-overlapping single speaker and overlapping multi-speaker conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2244–2255},
numpages = {12},
keywords = {neural network, speech recognition, beamforming, speech separation, microphone array}
}

@article{10.1109/TASLP.2014.2359626,
author = {Yong, Pei Chee and Nordholm, Sven and Dam, Hai Huyen},
title = {Effective Binaural Multi-Channel Processing Algorithm for Improved Environmental Presence},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2359626},
doi = {10.1109/TASLP.2014.2359626},
abstract = {Binaural noise-reduction algorithms based on multi-channel Wiener filter (MWF) are promising techniques to be used in binaural assistive listening devices. The real-time implementation of the existing binaural MWF methods, however, involves challenges to increase the amount of noise reduction without imposing speech distortion, and at the same time preserving the binaural cues of both speech and noise components. Although significant efforts have been made in the literature, most developed methods so far have focused only on either the former or latter problem. This paper proposes an alternative binaural MWF algorithm that incorporates the nonstationarity of the signal components into the framework. The main objective is to design an algorithm that would be able to select the sources that are present in the environment. To achieve this, a modified speech presence probability (SPP) and a single-channel speech enhancement algorithm are utilized in the formulation. The resulting optimal filter also avoids the poor estimation of the second-order clean speech statistics, which is normally done by simple subtraction. Theoretical analysis and performance evaluation using realistic recorded data shows the advantage of the proposed method over the reference MWF solution in terms of the binaural cues preservation, as well as the noise reduction and speech distortion.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2012–2024},
numpages = {13},
keywords = {single-channel noise reduction, multi-channel wiener filter, binaural cues, speech enhancement, modified sigmoid function}
}

@article{10.1109/TASLP.2014.2347133,
author = {Weng, Chao and Thomson, David L. and Haffner, Patrick and Juang, Biing-Hwang},
title = {Latent Semantic Rational Kernels for Topic Spotting on Conversational Speech},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2347133},
doi = {10.1109/TASLP.2014.2347133},
abstract = {In this work, we propose latent semantic rational kernels (LSRK) for topic spotting on conversational speech. Rather than mapping the input weighted finite-state transducers (WFSTs) onto a high dimensional n-gram feature space as in n-gram rational kernels, the proposed LSRK maps the WFSTs onto a latent semantic space. With the proposed LSRK, all available external knowledge and techniques can be flexibly integrated into a unified WFST based framework to boost the topic spotting performance. We present how to generalize the LSRK using tf-idf weighting, latent semantic analysis, WordNet and probabilistic topic models. To validate the proposed LSRK framework, we conduct the topic spotting experiments on two datasets, Switchboard and AT&T HMIHY0300 initial collection. The experimental results show that with the proposed LSRK we can achieve significant and consistent topic spotting performance gains over the n-gram rational kernels.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1738–1749},
numpages = {12},
keywords = {topic spotting, tf-idf, LSA, WFSTs, LDA, PLSA, rational kernels}
}

@article{10.1109/TASLP.2014.2352556,
author = {D'Angelo, Stefano and V\"{a}lim\"{a}ki, Vesa},
title = {Generalized Moog Ladder Filter: Part II-Explicit Nonlinear Model through a Novel Delay-Free Loop Implementation Method},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2352556},
doi = {10.1109/TASLP.2014.2352556},
abstract = {One of the most critical aspects of virtual analog simulation of circuits for music production consists in accurate reproduction of their nonlinear behavior, yet this goal is in many cases difficult to achieve due to the presence of implicit differential equations in circuit models, since they naturally map to delay-free loops in the digital domain. This paper presents a novel and general method for non-iteratively implementing these loops in such a way that the linear response around a chosen operating point is preserved, the topology is minimally affected, and transformation of nonlinearities is not required. This technique is then applied to a generalized model of the Moog ladder filter, resulting in an implementation that outperforms its predecessors with only a modest computational load penalty. This digital version of the filter is shown to offer strong stability guarantees w.r.t. parameter variation, allows the extraction of different frequency response modes by simple mixing of individual ladder stage outputs, and is suitable for real-time sound synthesis and audio effects processing.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1873–1883},
numpages = {11},
keywords = {circuit simulation, music, resonator filters, IIR filters, acoustic signal processing}
}

@article{10.1109/TASLP.2014.2347135,
author = {Davies, Matthew E. P. and Hamel, Philippe and Yoshii, Kazuyoshi and Goto, Masataka},
title = {AutoMashUpper: Automatic Creation of Multi-Song Music Mashups},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2347135},
doi = {10.1109/TASLP.2014.2347135},
abstract = {In this paper we present a system, AutoMashUpper, for making multi-song music mashups. Central to our system is a measure of "mashability" calculated between phrase sections of an input song and songs in a music collection. We define mashability in terms of harmonic and rhythmic similarity and a measure of spectral balance. The principal novelty in our approach centres on the determination of how elements of songs can be made fit together using key transposition and tempo modification, rather than based on their unaltered properties. In this way, the properties of two songs used to model their mashability can be altered with respect to transformations performed to maximize their perceptual compatibility. AutoMashUpper has a user interface to allow users to control the parameterization of the mashability estimation. It allows users to define ranges for key shifts and tempo as well as adding, changing or removing elements from the created mashups. We evaluate AutoMashUpper by its ability to reliably segment music signals into phrase sections, and also via a listening test to examine the relationship between estimated mashability and user enjoyment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1726–1737},
numpages = {12},
keywords = {music signal processing, music remixing, audio user interfaces, creative MIR}
}

@article{10.1109/TASLP.2014.2359159,
author = {Chen, Jitong and Wang, Yuxuan and Wang, DeLiang},
title = {A Feature Study for Classification-Based Speech Separation at Low Signal-to-Noise Ratios},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2359159},
doi = {10.1109/TASLP.2014.2359159},
abstract = {Speech separation can be formulated as a classification problem. In classification-based speech separation, supervised learning is employed to classify time-frequency units as either speech-dominant or noise-dominant. In very low signal-to-noise ratio (SNR) conditions, acoustic features extracted from a mixture are crucial for correct classification. In this study, we systematically evaluate a range of promising features for classification-based separation using six nonstationary noises at the low SNR level of -5 dB, which is chosen with the goal of improving human speech intelligibility in mind. In addition, we propose a new feature called multi-resolution cochleagram (MRCG). The new feature is constructed by combining four cochleagrams at different spectrotemporal resolutions in order to capture both the local and contextual information. Experimental results show that MRCG gives the best classification results among all evaluated features. In addition, our results indicate that auto-regressive moving average (ARMA) filtering, a post-processing technique for improving automatic speech recognition features, also improves many acoustic features for speech separation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1993–2002},
numpages = {10},
keywords = {speech separation, ARMA filtering, multi-resolution cochleagram, classification}
}

@article{10.1109/TASLP.2014.2357676,
author = {Foster, Peter and Mauch, Matthias and Dixon, Simon},
title = {Sequential Complexity as a Descriptor for Musical Similarity},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2357676},
doi = {10.1109/TASLP.2014.2357676},
abstract = {We propose string compressibility as a descriptor of temporal structure in audio, for the purpose of determining musical similarity. Our descriptors are based on computing trackwise compression rates of quantized audio features, using multiple temporal resolutions and quantization granularities. To verify that our descriptors capture musically relevant information, we incorporate our descriptors into similarity rating prediction and song year prediction tasks. We base our evaluation on a dataset of 15500 track excerpts of Western popular music, for which we obtain 7800 web-sourced pairwise similarity ratings. To assess the agreement among similarity ratings, we perform an evaluation under controlled conditions, obtaining a rank correlation of 0.33 between intersected sets of ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1965–1977},
numpages = {13},
keywords = {musical similarity measures, music content analysis, time series complexity}
}

@article{10.1109/TASLP.2014.2344856,
author = {Reddy, Vinod Veera and Khong, Andy W. H. and Ng, Boon Poh},
title = {Unambiguous Speech DOA Estimation under Spatial Aliasing Conditions},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2344856},
doi = {10.1109/TASLP.2014.2344856},
abstract = {With the bandwidth of speech signals extending over several octaves, the spatial Nyquist criterion constrains the microphone array design. Violating this criterion by increasing microphone spacing in order to achieve high resolution introduces ambiguity in identifying the source directions due to the aliasing components. In this work, we investigate the effect of spatial aliasing on the direction-of-arrival (DOA) spectrum due to wideband sources. Noting that the extent of aliasing is frequency dependent, we propose a multi-stage scheme for speech DOA estimation following a subband decomposition. To observe the advantage of this scheme, we verify it with the steered minimum variance distortionless response (STMV) and approximate kernel density estimators. The performance is evaluated with simulations and recorded room impulse responses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2133–2145},
numpages = {13},
keywords = {spatial aliasing, DOA estimation}
}

@article{10.1109/TASLP.2014.2363410,
author = {Han, Kun and Wang, DeLiang},
title = {Neural Network Based Pitch Tracking in Very Noisy Speech},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2363410},
doi = {10.1109/TASLP.2014.2363410},
abstract = {Pitch determination is a fundamental problem in speech processing, which has been studied for decades. However, it is challenging to determinate pitch in strong noise because the harmonic structure is corrupted. In this paper, we estimate pitch using supervised learning, where the probabilistic pitch states are directly learned from noisy speech data. We investigate two alternative neural networks modeling pitch state distribution given observations. The first one is a feedforward deep neural network (DNN), which is trained on static frame-level acoustic features. The second one is a recurrent deep neural network (RNN) which is trained on sequential frame-level features and capable of learning temporal dynamics. Both DNNs and RNNs produce accurate probabilistic outputs of pitch states, which are then connected into pitch contours by Viterbi decoding. Our systematic evaluation shows that the proposed pitch tracking algorithms are robust to different noise conditions and can even be applied to reverberant speech. The proposed approach also significantly outperforms other state-of-the-art pitch tracking algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2158–2168},
numpages = {11},
keywords = {recurrent neural networks (RNNs), deep neural networks (DNNs), pitch estimation, supervised learning, viterbi decoding}
}

@article{10.1109/TASLP.2014.2355774,
author = {Panagakis, Yannis and Kotropoulos, Constantine L. and Arce, Gonzalo R.},
title = {Music Genre Classification via Joint Sparse Low-Rank Representation of Audio Features},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2355774},
doi = {10.1109/TASLP.2014.2355774},
abstract = {A novel framework for music genre classification, namely the joint sparse low-rank representation (JSLRR) is proposed in order to: 1) smooth the noise in the test samples, and 2) identify the subspaces that the test samples lie onto. An efficient algorithm is proposed for obtaining the JSLRR and a novel classifier is developed, which is referred to as the JSLRR-based classifier. Special cases of the JSLRR-based classifier are the joint sparse representation-based classifier and the low-rank representation-based one. The performance of the three aforementioned classifiers is compared against that of the sparse representation-based classifier, the nearest subspace classifier, the support vector machines, and the nearest neighbor classifier for music genre classification on six manually annotated benchmark datasets. The best classification results reported here are comparable with or slightly superior than those obtained by the state-of-the-art music genre classification methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1905–1917},
numpages = {13},
keywords = {music genre classification, nuclear norm minimization, l1 norm minimization, low-rank representation, auditory representations, sparse representation}
}

@article{10.1109/TASLP.2014.2351614,
author = {Gil-Cacho, Jose M. and Van Waterschoot, Toon and Moonen, Marc and Jensen, S\o{}ren Holdt},
title = {A Frequency-Domain Adaptive Filter (FDAF) Prediction Error Method (PEM) Framework for Double-Talk-Robust Acoustic Echo Cancellation},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2351614},
doi = {10.1109/TASLP.2014.2351614},
abstract = {In this paper, we propose a new framework to tackle the double-talk (DT) problem in acoustic echo cancellation (AEC). It is based on a frequency-domain adaptive filter (FDAF) implementation of the so-called prediction error method adaptive filtering using row operations (PEM-AFROW) leading to the FDAF-PEM-AFROW algorithm. We show that FDAF-PEM-AFROW is by construction related to the best linear unbiased estimate (BLUE) of the echo path. We depart from this framework to show an improvement in performance with respect to other adaptive filters minimizing the BLUE criterion, namely the PEM-AFROW and the FDAF-NLMS with near-end signal normalization. One of the contributions is to propose the instantaneous pseudo-correlation (IPC) measure between the near-end signal and the loudspeaker signal. The IPC measure serves as an indication of the effect of a DT situation occurring during adaptation. We motivate the choice of FDAF-PEM-AFROW over PEM-AFROW and FDAF-NLMS with near-end signal normalization, based on performance, computational complexity and related IPC measure values. Moreover, we use the FDAF-PEM-AFROW framework to improve several state-of-the-art variable step-size (VSS) and variable regularization (VR) algorithms. The FDAF-PEM-AFROW versions significantly outperform the original versions in every simulation. In terms of computational complexity, the FDAF-PEM-AFROW versions are themselves about two orders of magnitude cheaper than the original versions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2074–2086},
numpages = {13},
keywords = {acoustic echo cancellation, double-talk-robust acoustic echo cancellation, double-talk, variable step size, prediction error method, frequency-domain adaptive filters}
}

@article{10.1109/TASLP.2014.2360646,
author = {Kim, Seon Man and Kim, Hong Kook},
title = {Direction-of-Arrival Based SNR Estimation for Dual-Microphone Speech Enhancement},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2360646},
doi = {10.1109/TASLP.2014.2360646},
abstract = {In this paper, we propose a method for estimating target speech by exploring the spatial cues in adverse noise environments. This method is able to reliably estimate the signal-to-noise ratio (SNR) using the phase difference obtained from dual-microphone signals. To this end, spatial cues such as the phase difference are used to estimate the target-to-non-target directional signal ratio (TNR). Based on the estimated TNR, a direction-of-arrival (DOA)-based SNR is then estimated by using a statistical model-based log-likelihood ratio test for the target speech activity decision followed by a decision-directed approach. The estimate is then incorporated into a Wiener filter in order to obtain a spectral-gain attenuator. The perceptual evaluation of speech quality shows that the performance of a dual-microphone speech enhancement system employing the proposed estimation method outperforms single-and dual-microphone speech enhancement systems that use conventional methods such as Wiener filtering, beamforming, or phase-error-based filtering under noise conditions whose SNR ranges from 0 to 20 dB.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2207–2217},
numpages = {11},
keywords = {signal-to-noise ratio, spatial cue, direction-of-arrival, dual-microphone signal, speech enhancement, target-to-non-target directional signal ratio}
}

@article{10.1109/TASLP.2014.2360643,
author = {Huang, Gongping and Benesty, Jacob and Long, Tao and Chen, Jingdong},
title = {A Family of Maximum SNR Filters for Noise Reduction},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2360643},
doi = {10.1109/TASLP.2014.2360643},
abstract = {This paper is devoted to the study and analysis of the maximum signal-to-noise ratio (SNR) filters for noise reduction both in the time and short-time Fourier transform (STFT) domains with one single microphone and multiple microphones. In the time domain, we show that the maximum SNR filters can significantly increase the SNR but at the expense of tremendous speech distortion. As a consequence, the speech quality improvement, measured by the perceptual evaluation of speech quality (PESQ) algorithm, is marginal if any, regardless of the number of microphones used. In the STFT domain, the maximum SNR filters are formulated by considering the interframe information in every frequency band. It is found that these filters not only improve the SNR, but also improve the speech quality significantly. As the number of input channels increases so is the gain in SNR as well as the speech quality. This demonstrates that the maximum SNR filters, particularly the multichannel ones, in the STFT domain may be of great practical value.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2034–2047},
numpages = {14},
keywords = {short-time Fourier transform (STFT) domain, time domain, multichannel, maximum SNR filter, speech enhancement, noise reduction, single channel}
}

@article{10.1109/TASLP.2014.2363788,
author = {Santos, Jo\~{a}o F. and Falk, Tiago H.},
title = {Updating the SRMR-CI Metric for Improved Intelligibility Prediction for Cochlear Implant Users},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2363788},
doi = {10.1109/TASLP.2014.2363788},
abstract = {When compared to intrusive speech intelligibility metrics, non-intrusive ones show a stronger dependency on speech content, given the lack of a reference signal for distortion level computation. Reduction of this dependency is an important step needed to develop reliable metrics. In this paper, two different updates to SRMR-CI, a recently-proposed speech intelligibility metric tailored for cochlear implant users, are applied. First, modulation energy thresholding is proposed to reduce the variability caused by the differences in modulation spectral representations for different phonemes and speakers, as well as speech enhancement algorithm artifacts. Second, a narrower range of modulation filters is employed to reduce fundamental frequency effects. Experimental results show that the updated metric outperforms two benchmark metrics, namely ModA and ANIQUE , by as much as 15% in terms of correlation between objective and subjective ratings, and a relative decrease of 47% in root mean square error compared to the previously-proposed SRMR-CI metric.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2197–2206},
numpages = {10},
keywords = {non-intrusive, speech intelligibility, modulation spectrum, objective metrics, cochlear implants}
}

@article{10.1109/TASLP.2014.2351132,
author = {Cobos, Maximo and Perez-Solano, Juan J. and Felici-Castell, Santiago and Segura, Jaume and Navarro, Juan M.},
title = {Cumulative-Sum-Based Localization of Sound Events in Low-Cost Wireless Acoustic Sensor Networks},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2351132},
doi = {10.1109/TASLP.2014.2351132},
abstract = {Wireless acoustic sensor networks (WASNs) are known for their potential applications in multiple areas, such as audio-based surveillance, binaural hearing aids or advanced acoustic monitoring. The knowledge of the spatial position of a source of interest is usually a requirement for many of these applications. Therefore, source localization is an important problem to be addressed in WASNs. Unfortunately, most localization algorithms need costly signal processing stages that prevent them from being implemented in low-cost sensor networks, requiring additional modules for signal acquisition and processing. This paper presents a low-complexity method for acoustic event detection and localization considering a change detection statistical framework. Two possible implementation approaches based on the efficient cumulative sum (CUSUM) algorithm are presented and discussed. Results from simulations and a real deployment show that the proposed techniques can be easily implemented in low-cost sensor networks, providing good localization accuracy and making good use of the available node resources.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1792–1802},
numpages = {11},
keywords = {wireless sensor networks, source localization, cumulative sum}
}

@article{10.1109/TASLP.2014.2359628,
author = {Chen, Austin and Hasegawa-Johnson, Mark A.},
title = {Mixed Stereo Audio Classification Using a Stereo-Input Mixed-to-Panned Level Feature},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2359628},
doi = {10.1109/TASLP.2014.2359628},
abstract = {Many past studies have been conducted on speech/music discrimination due to the potential applications for broadcast and other media; however, it remains possible to expand the experimental scope to include samples of speech with varying amounts of background music. This paper focuses on the development and evaluation of two measures of the ratio between speech energy and music energy: a reference measure called speech-to-music ratio (SMR), which is known objectively only prior to mixing, and a feature called the stereo-input mix-to-peripheral level feature (SIMPL), which is computed from the stereo mixed signal as an imprecise estimate of SMR. SIMPL is an objective signal measure calculated by taking advantage of broadcast mixing techniques in which vocals are typically placed at stereo center, unlike most instruments. Conversely, SMR is a hidden variable defined by the relationship between the powers of portions of audio attributed to speech and music. It is shown that SIMPL is predictive of SMR and can be combined with state-of-the-art features in order to improve performance. For evaluation, this new metric is applied in speech/music (binary) classification, speech/music/mixed (trinary) classification, and a new speech-to-music ratio estimation problem. Promising results are achieved, including 93.06% accuracy for trinary classification and 3.86 dB RMSE for estimation of the SMR.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2025–2033},
numpages = {9},
keywords = {mel-frequency cepstral coefficients, music processing, speech/music discrimination, speech processing, audio segmentation, Gaussian mixture model, classification algorithms, music information retrieval, audio classification, audio processing}
}

@article{10.1109/TASLP.2014.2361023,
author = {Jiang, Yi and Wang, DeLiang and Liu, RunSheng and Feng, ZhenMing},
title = {Binaural Classification for Reverberant Speech Segregation Using Deep Neural Networks},
year = {2014},
issue_date = {December 2014},
publisher = {IEEE Press},
volume = {22},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2361023},
doi = {10.1109/TASLP.2014.2361023},
abstract = {Speech signal degradation in real environments mainly results from room reverberation and concurrent noise. While human listening is robust in complex auditory scenes, current speech segregation algorithms do not perform well in noisy and reverberant environments. We treat the binaural segregation problem as binary classification, and employ deep neural networks (DNNs) for the classification task. The binaural features of the interaural time difference and interaural level difference are used as the main auditory features for classification. The monaural feature of gammatone frequency cepstral coefficients is also used to improve classification performance, especially when interference and target speech are collocated or very close to one another. We systematically examine DNN generalization to untrained spatial configurations. Evaluations and comparisons show that DNN-based binaural classification produces superior segregation performance in a variety of multisource and reverberant conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2112–2121},
numpages = {10},
keywords = {room reverberation, computational auditory scene analysis (CASA), speech segregation, deep neural networks (DNNs), binary classification}
}

@article{10.1109/TASLP.2014.2344862,
author = {Defraene, Bruno and Van Waterschoot, Toon and Diehl, Moritz and Moonen, Marc},
title = {Embedded-Optimization-Based Loudspeaker Precompensation Using a Hammerstein Loudspeaker Model},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2344862},
doi = {10.1109/TASLP.2014.2344862},
abstract = {This paper presents an embedded-optimization-based loudspeaker precompensation algorithm using a Hammerstein loudspeaker model, i.e. a cascade of a memoryless nonlinearity and a linear finite impulse response filter. The loudspeaker precompensation consists in a per-frame signal optimization. In order to minimize the perceptible distortion incurred in the loudspeaker, a psychoacoustically motivated optimization criterion is proposed. The resulting per-frame signal optimization problems are solved efficiently using first-order optimization methods. Depending on the invertibility and the smoothness of the memoryless nonlinearity, different first-order optimization methods are proposed and their convergence properties are analyzed. Objective evaluation experiments using synthetic loudspeaker models and real loudspeakers show that the proposed loudspeaker precompensation algorithm provides a significant audio quality improvement, especially so at high playback levels.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1648–1659},
numpages = {12},
keywords = {loudspeaker precompensation, sound perception, gradient optimization, hammerstein model, embedded optimization}
}

@article{10.1109/TASLP.2014.2341918,
author = {Togami, Masahito and Kawaguchi, Yohei},
title = {Simultaneous Optimization of Acoustic Echo Reduction, Speech Dereverberation, and Noise Reduction against Mutual Interference},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2341918},
doi = {10.1109/TASLP.2014.2341918},
abstract = {We propose an optimized speech enhancement method that combines acoustic echo reduction, speech dereverberation, and noise reduction in a unified framework. Normally, partial optimization of acoustic echo reduction, speech dereverberation, and noise reduction does not lead to total optimization. A cascade method of multiple functions causes mutual interference between these functions and degrades eventual speech enhancement performance. Unlike cascade methods, the proposed method combines all functions to optimize eventual speech enhancement performance based on a unified framework, which is also robust against the mutual interference problem. With the proposed method, in addition to time-invariant linear filters, time-varying filters are used to reduce residual reverberation, residual acoustic echo signal, and background noise signal which cannot be reduced using time-invariant filters. These time-invariant filters and time-varying filters are also optimized based on a unified likelihood function to avoid the mutual interference problem. By combining the time-invariant linear filters and the time-varying filters, the proposed method uses a local Gaussian model with a full-rank covariance matrix and a non-zero average vector as a probabilistic model of the microphone input signal. In the local Gaussian model, non-stationary characteristics of speech sources are considered to effectively enhance speech sources. Under this probabilistic model, all the parameters are optimized simultaneously based on the expectation-maximization algorithm and calculates a minimum mean squared error estimate of a desired signal. The experimental results show that the proposed method is superior to the cascade methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1612–1623},
numpages = {12},
keywords = {simultaneous optimization, echo reduction, dereverberation, local Gaussian modeling}
}

@article{10.1109/TASLP.2014.2341920,
author = {Badeau, Roland and Plumbley, Mark D.},
title = {Multichannel High-Resolution NMF for Modeling Convolutive Mixtures of Non-Stationary Signals in the Time-Frequency Domain},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2341920},
doi = {10.1109/TASLP.2014.2341920},
abstract = {Several probabilistic models involving latent components have been proposed for modeling time-frequency (TF) representations of audio signals such as spectrograms, notably in the nonnegative matrix factorization (NMF) literature. Among them, the recent high-resolution NMF (HR-NMF) model is able to take both phases and local correlations in each frequency band into account, and its potential has been illustrated in applications such as source separation and audio inpainting. In this paper, HR-NMF is extended to multichannel signals and to convolutive mixtures. The new model can represent a variety of stationary and non-stationary signals, including autoregressive moving average (ARMA) processes and mixtures of damped sinusoids. A fast variational expectation-maximization (EM) algorithm is proposed to estimate the enhanced model. This algorithm is applied to piano signals, and proves capable of accurately modeling reverberation, restoring missing observations, and separating pure tones with close frequencies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1670–1680},
numpages = {11},
keywords = {non-stationary signal modeling, multichannel signal analysis, variational EM algorithm, nonnegative matrix factorization, time-frequency analysis}
}

@article{10.1109/TASLP.2014.2341911,
author = {Xu, Jian and Yan, Zhi-Jie and Huo, Qiang},
title = {An Unsupervised Adaptation Approach to Leveraging Feedback Loop Data by Using I-Vector for Data Clustering and Selection},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2341911},
doi = {10.1109/TASLP.2014.2341911},
abstract = {We present a study of using unsupervised adaptation approaches to improve speech recognition accuracy of a deployed speech service by leveraging large-scale untranscribed speech data collected from a feedback loop (FBL). For a regular user with lots of adaptation utterances, conventional CMLLR-based adaptation can be used for personalization directly. For a casual user with a few adaptation utterances, we propose to use CMLLR-based adaptation by augmenting his / her adaptation utterances with utterances acoustically close to the user, which are selected from the FBL data by an i-vector based approach. For a new user, we propose to perform a CMLLR-based recognition of an unknown utterance by selecting a set of CMLLR transforms from the most similar cluster, which are pre-trained by using the utterances from the corresponding cluster generated by an i-vector based utterance clustering method from the FBL data. The effectiveness of the above approaches are confirmed by our experiments on a short message dictation task on smart phones.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1581–1589},
numpages = {9},
keywords = {feedback loop, unsupervised adaptation, personalization, data augmentation, i-vector, data clustering

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repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr