text:
}
}

@article{10.1109/TASLP.2014.2341914,
author = {Cumani, Sandro and Laface, Pietro},
title = {Large-Scale Training of Pairwise Support Vector Machines for Speaker Recognition},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2341914},
doi = {10.1109/TASLP.2014.2341914},
abstract = {State-of-the-art systems for text-independent speaker recognition use as their features a compact representation of a speaker utterance, known as "i-vector." We recently presented an efficient approach for training a Pairwise Support Vector Machine (PSVM) with a suitable kernel for i-vector pairs for a quite large speaker recognition task. Rather than estimating an SVM model per speaker, according to the "one versus all" discriminative paradigm, the PSVM approach classifies a trial, consisting of a pair of i-vectors, as belonging or not to the same speaker class. Training a PSVM with large amount of data, however, is a memory and computational expensive task, because the number of training pairs grows quadratically with the number of training i-vectors. This paper demonstrates that a very small subset of the training pairs is necessary to train the original PSVM model, and proposes two approaches that allow discarding most of the training pairs that are not essential, without harming the accuracy of the model. This allows dramatically reducing the memory and computational resources needed for training, which becomes feasible with large datasets including many speakers. We have assessed these approaches on the extended core conditions of the NIST 2012 Speaker Recognition Evaluation. Our results show that the accuracy of the PSVM trained with a sufficient number of speakers is 10%-30% better compared to the one obtained by a PLDA model, depending on the testing conditions. Since the PSVM accuracy increases with the training set size, but PSVM training does not scale well for large numbers of speakers, our selection techniques become relevant for training accurate discriminative classifiers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1590–1600},
numpages = {11},
keywords = {PLDA, support vectors, speaker recognition, i-vector, pairwise support vector machines}
}

@article{10.1109/TASLP.2014.2344852,
author = {Lorente, Jorge and Ferrer, Miguel and De Diego, Maria and Gonz\'{a}lez, Alberto},
title = {GPU Implementation of Multichannel Adaptive Algorithms for Local Active Noise Control},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2344852},
doi = {10.1109/TASLP.2014.2344852},
abstract = {Multichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1624–1635},
numpages = {12},
keywords = {filtered-x least mean square, active noise control, graphics processing unit}
}

@article{10.1109/TASLP.2014.2344855,
author = {Wang, Guangsen and Sim, Khe Chai},
title = {Regression-Based Context-Dependent Modeling of Deep Neural Networks for Speech Recognition},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2344855},
doi = {10.1109/TASLP.2014.2344855},
abstract = {The data sparsity problem is addressed by using the decision tree state clusters as the training targets for the state-of-the-art context-dependent (CD) deep neural network (DNN) systems. The CD states within a cluster cannot be distinguished at the frame level. We surmise that the state clustering may cause an issue for the standard CD-DNNs, which has so far not been addressed in the literature. In this paper, a logistic regression framework is proposed for the CD-DNNs based on a set of broad phone classes to address both the data sparsity and the clustering problems. To address the data sparsity issue, the triphones are clustered into shorter biphones with broad phone contexts under multiple articulatory categories. A DNN is trained to discriminate the disjoint biphone clusters within each articulatory category. The regression bases are formed by the concatenated log posterior probabilities of all the broad phone DNNs. Logistic regression is used to transform the regression bases into the triphone state posteriors. Clustering of the regression parameters is used to reduce the regression model complexity while still achieving unique acoustic scores for all possible triphones. Based on some approximations, the regression model can be trained as a sparse softmax layer and its parameters can be learned by optimizing the cross-entropy criterion. The experimental results on a broadcast news transcription task reveal that the proposed regression-based CD-DNN significantly outperforms the standard CD-DNN. The best system provides a 1.3% absolute word error rate reduction compared to the best standard CD-DNN system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1660–1669},
numpages = {10},
keywords = {context dependent modeling, deep neural network, logistic regression, articulatory features}
}

@article{10.1109/TASLP.2014.2341912,
author = {Du, Jun and Huo, Qiang},
title = {An Improved VTS Feature Compensation Using Mixture Models of Distortion and IVN Training for Noisy Speech Recognition},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2341912},
doi = {10.1109/TASLP.2014.2341912},
abstract = {In our previous work, we proposed a feature compensation approach using high-order vector Taylor series (VTS) approximation for noisy speech recognition. In this paper, we report new progress on making it more powerful and practical in real applications. First, mixtures of densities are used to enhance the distortion models of both additive noise and convolutional distortion. New formulations for maximum likelihood (ML) estimation of distortion model parameters, and minimum mean squared error (MMSE) estimation of clean speech are derived and presented. Second, we improve the feature compensation in both efficiency and accuracy by applying higher order information of VTS approximation only to the noisy speech mean parameters, and a temporal smoothing operation for the posterior probability of Gaussian mixture components in clean speech estimation. Finally, we design a procedure to perform irrelevant variability normalization (IVN) based joint training of a reference Gaussian mixture model (GMM) for feature compensation and hidden Markov models (HMMs) for acoustic modeling using VTS-based feature compensation. The effectiveness of our proposed approach is confirmed by experiments on Aurora3 benchmark database for a real-world in-vehicle connected digits recognition task. Compared with ETSI advanced front-end, our approach achieves significant recognition accuracy improvement across three "training-testing" conditions for four languages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1601–1611},
numpages = {11},
keywords = {irrelevant variability normalization, mixture model of distortion, noisy speech recognition, vector Taylor series, feature compensation}
}

@article{10.1109/TASLP.2014.2323715,
author = {H\'{e}lie, Thomas},
title = {Simulation of Fractional-Order Low-Pass Filters},
year = {2014},
issue_date = {November 2014},
publisher = {IEEE Press},
volume = {22},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2323715},
doi = {10.1109/TASLP.2014.2323715},
abstract = {The attenuation of standard analog low-pass filters corresponds to a multiple value of decibels per octave. This quantified value is related to the order of the filter. The issue addressed here is concerned with the extension of integer orders to non integer orders, such that the attenuation of a low-pass filter can be continuously adjusted. Fractional differential systems are known to provide such asymptotic behaviors and many results about their simulation are available. But even for a fixed cutoff frequency, their combination does not generate an additive group with respect to the order and they involve stability problems. In this paper, a class of low-pass filters with orders between 0 (the filter is a unit gain) and 1 (standard one-pole filter) is defined to restore these properties. These infinite dimensional filters are not fractional differential but admit some well-posed representations into weighted integrals of standard one-pole filters. Based on this, finite dimensional approximations are proposed and recast into the framework of state-space representations. A special care is given to reduce the computational complexity, through the dimension of the state. In practice, this objective is reached for the complete family, without damaging the perceptive quality, with dimension 13. Then, an accurate low-cost digital version of this family is built in the time-domain. The accuracy of the digital filters is verified on the complete range of parameters (cutoff frequencies and fractional orders). Moreover, the stability is guaranteed, even for time-varying parameters. As an application, a plugin has been implemented which provides a new audio tool for tuning the cutoff frequency and the asymptotic slope in a continuous way. As a very special application, choosing a one-half order combined with a low cutoff frequency (20 Hz or less), the filter fed with a white noise provides a pink noise generator.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1636–1647},
numpages = {12},
keywords = {irrational transfer function, simulation, fractional order, signal synthesis, state-space methods}
}

@article{10.1109/TASLP.2014.2339736,
author = {Abdel-Hamid, Ossama and Mohamed, Abdel-Rahman and Jiang, Hui and Deng, Li and Penn, Gerald and Yu, Dong},
title = {Convolutional Neural Networks for Speech Recognition},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2339736},
doi = {10.1109/TASLP.2014.2339736},
abstract = {Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structure such as local connectivity, weight sharing, and pooling in CNNs exhibits some degree of invariance to small shifts of speech features along the frequency axis, which is important to deal with speaker and environment variations. Experimental results show that CNNs reduce the error rate by 6%-10% compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1533–1545},
numpages = {13},
keywords = {pooling, convolution, convolutional neural networks, limited weight sharing (LWS) scheme}
}

@article{10.1109/TASLP.2014.2337844,
author = {Zhao, Liheng and Benesty, Jacob and Chen, Jingdong},
title = {Design of Robust Differential Microphone Arrays},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2337844},
doi = {10.1109/TASLP.2014.2337844},
abstract = {Differential microphone arrays (DMAs), due to their small size and enhanced directivity, are quite promising in speech enhancement applications. However, it is well known that differential beamformers have the drawback of white noise amplification, which is a major issue in the processing of wideband signals such as speech. In this paper, we focus on the design of robust DMAs. Based on the Maclaurin's series approximation and frequency-independent beampatterns, the robust first-, second-, and third-order DMAs are proposed by using more microphones than the order plus one, and the corresponding minimum-norm filters are derived. Compared to the traditional DMAs, the proposed designs are more robust with respect to white noise amplification while they are capable of achieving similar directional gains.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1455–1466},
numpages = {12},
keywords = {beamforming, directivity factor, second-order DMA, white noise gain, beampattern, first-order DMA, differential microphone arrays (DMAs), third-order DMA, robust DMAs}
}

@article{10.1109/TASLP.2014.2339738,
author = {Chen, Chia-Ping and Huang, Yi-Chin and Wu, Chung-Hsien and Lee, Kuan-De},
title = {Polyglot Speech Synthesis Based on Cross-Lingual Frame Selection Using Auditory and Articulatory Features},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2339738},
doi = {10.1109/TASLP.2014.2339738},
abstract = {In this paper, an approach for polyglot speech synthesis based on cross-lingual frame selection is proposed. This method requires only mono-lingual speech data of different speakers in different languages for building a polyglot synthesis system, thus reducing the burden of data collection. Essentially, a set of artificial utterances in the second language for a target speaker is constructed based on the proposed cross-lingual frame-selection process, and this data set is used to adapt a synthesis model in the second language to the speaker. In the cross-lingual frame-selection process, we propose to use auditory and articulatory features to improve the quality of the synthesized polyglot speech. For evaluation, a Mandarin-English polyglot system is implemented where the target speaker only speaks Mandarin. The results show that decent performance regarding voice identity and speech quality can be achieved with the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1558–1570},
numpages = {13},
keywords = {cross-lingual frame selection, auditory features, articulatory features, polyglot speech synthesis}
}

@article{10.1109/TASLP.2014.2339735,
author = {Koyama, Shoichi and Furuya, Ken'ichi and Hiwasaki, Yusuke and Haneda, Yoichi and Suzuki, Y\^{o}iti},
title = {Wave Field Reconstruction Filtering in Cylindrical Harmonic Domain for With-Height Recording and Reproduction},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2339735},
doi = {10.1109/TASLP.2014.2339735},
abstract = {For sound field reproduction that includes height (with-height reproduction), it is more efficient to record and reproduce the sound field with lower resolution in elevation than in azimuth due to the spatial abilities of human auditory perception. We propose a sound field reproduction method using horizontally arranged cylindrical arrays of microphones and loudspeakers, which is based on the wave field reconstruction (WFR) filter. With the use of cylindrical array configurations, it is possible to reproduce sound waves arriving from upper and lower directions with a smaller number of array elements at angular positions. The WFR filter is analytically derived in the cylindrical harmonic domain and allows direct transformation from the received signals of the microphones into the driving signals of the loudspeakers. A model in which microphones are mounted on a rigid cylindrical baffle is introduced to stabilize the WFR filter. Numerical simulation results indicated that the reproduction accuracy in the neighboring region along the central axis of the cylindrical array was better preserved when using the proposed method than when the method with planar arrays was used.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1546–1557},
numpages = {12},
keywords = {cylindrical harmonics, helical wave spectrum, higher order ambisonics, wave field reconstruction filter, sound field reproduction, wave field synthesis, Fourier transform}
}

@article{10.1109/TASLP.2014.2337846,
author = {Nadiri, O. and Rafaely, B.},
title = {Localization of Multiple Speakers under High Reverberation Using a Spherical Microphone Array and the Direct-Path Dominance Test},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2337846},
doi = {10.1109/TASLP.2014.2337846},
abstract = {One of the major challenges encountered when localizing multiple speakers in real world environments is the need to overcome the effect of multipath distortion due to room reverberation. A wide range of methods has been proposed for speaker localization, many based on microphone array processing. Some of these methods are designed for the localization of coherent sources, typical of multipath environments, and some have even reported limited robustness to reverberation. Nevertheless, speaker localization under conditions of high reverberation still remains a challenging task. This paper proposes a novel multiple-speaker localization technique suitable for environments with high reverberation, based on a spherical microphone array and processing in the spherical harmonics (SH) domain. The non-stationarity and sparsity of speech, as well as frequency smoothing in the SH domain, are exploited in the development of a direct-path dominance test. This test can identify time-frequency (TF) bins that contain contributions from only one significant source and no significant contribution from room reflections, such that localization based on these selected TF-bins is performed accurately, avoiding the potential distortion due to other sources and reverberation. Computer simulations and an experiment in a real reverberant room validate the robustness of the proposed method in the presence of high reverberation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1494–1505},
numpages = {12},
keywords = {time-frequency analysis, speaker localization, spherical array, direction-of-arrival estimation, room reverberation}
}

@article{10.1109/TASLP.2014.2339195,
author = {Talagala, Dumidu S. and Zhang, Wen and Abhayapala, Thushara D.},
title = {Efficient Multi-Channel Adaptive Room Compensation for Spatial Soundfield Reproduction Using a Modal Decomposition},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2339195},
doi = {10.1109/TASLP.2014.2339195},
abstract = {Mitigating the effects of reverberation is a significant challenge for real-world spatial soundfield reproduction, but the necessity of a large number of reproduction channels increases the complexity and presents several challenges to existing listening room compensation techniques. In this paper, we present an adaptive room compensation method to overcome the effects of reverberation within a region, using a model description of the reverberant soundfield. We propose the reverberant channel estimation and compensation be carried out in a single step using completely decoupled adaptive filters; thus, reducing the complexity of the overall process. We compare the soundfield reproduction performance with existing adaptive and nonadaptive room compensation methods through several simulation examples. The performance of the proposed method is comparable to existing techniques, and achieves a normalized wideband region reproduction error of 1% at a signal-to-noise ratio of 50 dB, within a 1 m radius region of interest using 60 loudspeakers and 55 microphones at frequencies below 1 kHz. Robust behavior of the room compensator is demonstrated down to direct-to-reverberant-path power ratios of -5 dB. Overall, the results suggest that the proposed method can diagonalize the room compensation system, leading to amore robust and parallel implementation for spatial soundfield reproduction.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1522–1532},
numpages = {11},
keywords = {modal decomposition, soundfield reproduction, reverberation, decoupled adaptive filtering, room equalization, multi-channel audio, adaptive channel estimation, room compensation}
}

@article{10.1109/TASLP.2014.2335056,
author = {Jain, Pooja and Pachori, Ram Bilas},
title = {Event-Based Method for Instantaneous Fundamental Frequency Estimation from Voiced Speech Based on Eigenvalue Decomposition of the Hankel Matrix},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2335056},
doi = {10.1109/TASLP.2014.2335056},
abstract = {We propose a robust event-based method for estimation of the instantaneous fundamental frequency of a voiced speech signal. The amplitude and frequency modulated (AM-FM) signal model of voiced speech in the low frequency range (LFR) indicates the presence of energy only around its instantaneous fundamental frequency (F0) and its few harmonics. The time-varying F0 component of a voiced speech signal is extracted by a robust algorithm which iteratively performs eigenvalue decomposition (EVD) of the Hankel matrix, initially constructed from samples of the LFR filtered voiced speech signal. The negative cycles of the extracted time-varying F0 component provide a reliable coarse estimate of intervals where glottal closure instants (GCIs) may be present. The negative cycles of the LFR filtered voiced speech signal occurring within these intervals are isolated. There is a sudden decrease in the glottal impedance at GCIs resulting in high signal strength. Therefore, GCIs are detected as local minima in the derivative of the falling edges of the isolated negative cycles of the LFR filtered voiced speech signal, followed by a selection criterion to discard false GCI candidates. The instantaneous F0 is estimated as the inverse of the time interval between two consecutive GCIs. Experiments were performed on the Keele and CSTR speech databases in white and babble noise environments at various levels of degradation to assess the performance of the proposed method. The proposed method substantially reduces the gross F0 estimation errors in comparison to some state of the art methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1467–1482},
numpages = {16},
keywords = {Hankel matrix, eigenvalue decomposition, instantaneous fundamental frequency, speech signal processing}
}

@article{10.1109/TASLP.2014.2333242,
author = {Wu, Zhizheng and Virtanen, Tuomas and Chng, Eng Siong and Li, Haizhou},
title = {Exemplar-Based Sparse Representation with Residual Compensation for Voice Conversion},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2333242},
doi = {10.1109/TASLP.2014.2333242},
abstract = {We propose a nonparametric framework for voice conversion, that is, exemplar-based sparse representation with residual compensation. In this framework, a spectrogram is reconstructed as a weighted linear combination of speech segments, called exemplars, which span multiple consecutive frames. The linear combination weights are constrained to be sparse to avoid over-smoothing, and high-resolution spectra are employed in the exemplars directly without dimensionality reduction to maintain spectral details. In addition, a spectral compression factor and a residual compensation technique are included in the framework to enhance the conversion performances. We conducted experiments on the VOICES database to compare the proposed method with a large set of state-of-the-art baseline methods, including the maximum likelihood Gaussian mixture model (ML-GMM) with dynamic feature constraint and the partial least squares (PLS) regression based methods. The experimental results show that the objective spectral distortion of ML-GMM is reduced from 5.19 dB to 4.92 dB, and both the subjective mean opinion score and the speaker identification rate are increased from 2.49 and 73.50% to 3.15 and 79.50%, respectively, by the proposed method. The results also show the superiority of our method over PLS-based methods. In addition, the subjective listening tests indicate that the naturalness of the converted speech by our proposed method is comparable with that by the ML-GMM method with global variance constraint.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1506–1521},
numpages = {16},
keywords = {sparse representation, voice conversion, residual compensation, nonnegative matrix factorization, exemplar}
}

@article{10.1109/TASLP.2014.2337842,
author = {Vaizman, Yonatan and McFee, Brian and Lanckriet, Gert},
title = {Codebook-Based Audio Feature Representation for Music Information Retrieval},
year = {2014},
issue_date = {October 2014},
publisher = {IEEE Press},
volume = {22},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2337842},
doi = {10.1109/TASLP.2014.2337842},
abstract = {Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on content based methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the audio content representation. Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely the LASSO, vector quantization (VQ) and cosine similarity (CS). We evaluate the representations' quality in two music information retrieval applications: query-by-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top-τ VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1483–1493},
numpages = {11},
keywords = {vector quantization, sparse coding, audio content representations, music recommendation, music information retrieval}
}

@article{10.1109/TASLP.2014.2328175,
author = {Xiang, Yong and Natgunanathan, Iynkaran and Guo, Song and Zhou, Wanlei and Nahavandi, Saeid},
title = {Patchwork-Based Audio Watermarking Method Robust to de-Synchronization Attacks},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2328175},
doi = {10.1109/TASLP.2014.2328175},
abstract = {This paper presents a patchwork-based audio watermarking method to resist de-synchronization attacks such as pitch-scaling, time-scaling, and jitter attacks. At the embedding stage, the watermarks are embedded into the host audio signal in the discrete cosine transform (DCT) domain. Then, a set of synchronization bits are implanted into the watermarked signal in the logarithmic DCT (LDCT) domain. At the decoding stage, we analyze the received audio signal in the LDCT domain to find the scaling factor imposed by an attack. Then, we modify the received signal to remove the scaling effect, together with the embedded synchronization bits. After that, watermarks are extracted from the modified signal. Simulation results show that at the embedding rate of 10 bps, the proposed method achieves 98.9% detection rate on average under the considered de-synchronization attacks. At the embedding rate of 16 bps, it can still obtain 94.7% detection rate on average. So, the proposed method is much more robust to de-synchronization attacks than other patchwork watermarking methods. Compared with the audio watermarking methods designed for tackling de-synchronization attacks, our method has much higher embedding capacity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1413–1423},
numpages = {11},
keywords = {audio watermarking, discrete cosine transform, patchwork, de-synchronization attack, synchronization bits}
}

@article{10.1109/TASLP.2014.2335055,
author = {McLoughlin, Ian Vince},
title = {Super-Audible Voice Activity Detection},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2335055},
doi = {10.1109/TASLP.2014.2335055},
abstract = {In this paper, reflected sound of frequency just above the audible range is used to detect speech activity. The active signal used is inaudible to humans, readily generated by the typical audio circuitry and components found in mobile telephones, and is robust to background sounds such as nearby voices. In use, the system relies upon a wideband excitation signal emitted from a loudspeaker located near the lips, which reflects from the mouth region and is then captured by a nearby microphone. The state of the lip opening is evaluated periodically by tracking the resonance patterns in the reflected excitation signal. When the lips are open, deep and complex resonances are formed as energy propagates into and then reflects out from the open mouth and vocal tract, with resonance depth being related to the open lip area. When the lips are closed, these resonance patterns are absent. The presence of the resonances can thus serve as a low complexity detection measure. The technique is evaluated for multiple users in terms of sensitivity to source placement and sensor placement. Voice activity detection performance using this measure is further evaluated in the presence of realistic wideband acoustic background noise, as well as artificially added noise. The system is shown to be relatively insensitive to sensor placement, highly insensitive to background noise, and able to achieve greater than 90% voice activity detection accuracy. The technique is even suitable when a subject is whispering in the presence of much louder multi-speaker babble. The technique has potential for speech-based systems operating in high noise environments as well as in silent speech interfaces, whisper-input systems and voice prostheses for speech-impaired users.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1424–1433},
numpages = {10},
keywords = {voice activity detection, silent speech interfaces, mouth state detection, speech activity detection, lip state detection, voice operated switch}
}

@article{10.1109/TASLP.2014.2332043,
author = {Ghalehjegh, Sina Hamidi and Rose, Richard C.},
title = {Linear Regression Based Acoustic Adaptation for the Subspace Gaussian Mixture Model},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2332043},
doi = {10.1109/TASLP.2014.2332043},
abstract = {This paper presents a study of two acoustic speaker adaptation techniques applied in the context of the subspace Gaussian mixture model (SGMM) for automatic speech recognition (ASR). First, a model space linear regression based approach is presented for adaptation of SGMM state projection vectors and is referred to as subspace vector adaptation (SVA). Second, an easy to implement realization of constrained maximum likelihood linear regression (CMLLR) is presented for feature space adaptation in the SGMM. Numerically stable procedures for row-by-row estimation of the regression based transformation matrices are presented for both SVA and CMLLR adaptation. These approaches are applied to SGMM models that are estimated using speaker adaptive training (SAT), a technique for estimating more compact speaker independent acoustic models. Unsupervised speaker adaptation performance is evaluated on conversational and read speech task domains and compared to unsupervised adaptation performance obtained using the hidden Markov model-Gaussian mixture model (HMM-GMM) in ASR. It is shown that the feature space and model space adaptation approaches applied to the SGMM provide complementary reductions in word error rate (WER) and provide lower WERs than that obtained using CMLLR adaptation for the HMM-GMM.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1391–1402},
numpages = {12},
keywords = {speaker adaptation, subspace modeling, constrained maximum likelihood linear regression, automatic speech recognition}
}

@article{10.1109/TASLP.2014.2329734,
author = {Novotn\'{y}, Michal and Rusz, Jan and \v{C}mejla, Roman and R\r{u}\v{z}i\v{c}ka, Ev\v{z}en},
title = {Automatic Evaluation of Articulatory Disorders in Parkinson's Disease},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329734},
doi = {10.1109/TASLP.2014.2329734},
abstract = {Although articulatory deficits represent an important manifestation of dysarthria in Parkinson's disease (PD), the most widely used methods currently available for the automatic evaluation of speech performance are focused on the assessment of dysphonia. The aim of the present study was to design a reliable automatic approach for the precise estimation of articulatory deficits in PD. Twenty-four individuals diagnosed with de novo PD and twenty-two age-matched healthy controls were recruited. Each participant performed diadochokinetic tasks based upon the fast repetition of /pa/-/ta/-/ka/ syllables. All phonemes were manually labeled and an algorithm for their automatic detection was designed. Subsequently, 13 features describing six different articulatory aspects of speech including vowel quality, coordination of laryngeal and supralaryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing were analyzed. In addition, a classification experiment using a support vector machine based on articulatory features was proposed to differentiate between PD patients and healthy controls. The proposed detection algorithm reached approximately 80% accuracy for a 5 ms threshold of absolute difference between manually labeled references and automatically detected positions. When compared to controls, PD patients showed impaired articulatory performance in all investigated speech dimensions (p < 0.05). Moreover, using the six features representing different aspects of articulation, the best overall classification result attained a success rate of 88% in separating PD from controls. Imprecise consonant articulation was found to be the most powerful indicator of PD-related dysarthria. We envisage our approach as the first step towards development of acoustic methods allowing the automated assessment of articulatory features in dysarthrias.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1366–1378},
numpages = {13},
keywords = {speech disorders, diadochokinetic task, hypokinetic dysarthria, acoustic analysis, automatic segmentation, Parkinson's disease}
}

@article{10.1109/TASLP.2014.2332045,
author = {Botts, Jonathan and Savioja, Lauri},
title = {Spectral and Pseudospectral Properties of Finite Difference Models Used in Audio and Room Acoustics},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2332045},
doi = {10.1109/TASLP.2014.2332045},
abstract = {Finite difference solutions to the wave equation are simple and flexible modeling tools for approximating physical systems in audio and room acoustics. Each model is characterized by a matrix operator and the time-stepping solution by a sequence of powers of the matrix. Spectral decomposition of representative matrices provide some practical insight into solution behavior and in some cases stability. In addition to computed eigenvalue spectra, pseudospectra provide a description of numerical amplification due to rounding errors in floating point arithmetic. The matrix analysis also shows that certain boundary implementations in non-cuboid geometries can be unstable despite satisfying conditions derived from von Neumann and normal mode analyses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1403–1412},
numpages = {10},
keywords = {eigenvalues and eigenfunctions, room acoustics, pseudospectra, operator spectra, finite difference}
}

@article{10.1109/TASLP.2014.2329184,
author = {Masiero, Bruno and Vorl\"{a}nder, Michael},
title = {A Framework for the Calculation of Dynamic Crosstalk Cancellation Filters},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329184},
doi = {10.1109/TASLP.2014.2329184},
abstract = {Dynamic crosstalk cancellation (CTC) systems commonly find use in immersive virtual reality (VR) applications. Such dynamic setups require extremely high filter update rates, so filter calculation is usually performed in the frequency-domain for higher efficiency. This paper proposes a general framework for the calculation of dynamic CTC filters to be used in immersive VR applications. Within this framework, we introduce a causality constraint to the frequency-domain calculation to avoid undesirable wrap-around effects and echo artifacts. Furthermore, when regularization is applied to the CTC filter calculation, in order to limit the output levels at the loudspeakers, noncausal artifacts appear at the CTC filters and the resulting ear signals. We propose a global minimum-phase regularization to convert these anti-causal ringing artifacts into causal artifacts. Finally, an aspect that is especially critical for dynamic CTC systems is the filter switch between active loudspeakers distributed in a surround audio-visual display system with 360° of freedom of operator orientation. Within this framework we apply a weighted filter calculation to control the filter switch, which allows the loudspeakers' contribution to be windowed in space, resulting in a smooth filter transition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1345–1354},
numpages = {10},
keywords = {minimum-phase regularization, binaural technique, dynamic crosstalk cancellation, causal implementation}
}

@article{10.1109/TASLP.2014.2329632,
author = {Lim, Felicia and Zhang, Wancheng and Habets, Emanu\"{e}l A. P. and Naylor, Patrick A.},
title = {Robust Multichannel Dereverberation Using Relaxed Multichannel Least Squares},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329632},
doi = {10.1109/TASLP.2014.2329632},
abstract = {A novel approach is proposed for robust multichannel dereverberation in the presence of system identification error (SIEs), based on channel shortening. A mathematical link is derived between the well known multiple-input/output inverse theorem (MINT) algorithm and channel shortening. The relaxed multichannel least squares (RMCLS) algorithm is then proposed as an efficient realization within the channel shortening paradigm and is shown through experimental results to outperform MINT in the presence of SIEs. While the RMCLS is robust to SIEs, the coloration of the output cannot be controlled. Two extensions to RMCLS are proposed to control the level of coloration and the performances of both extensions are evaluated comparatively. It is shown that both substantially maintain the dereverberation performance and robustness to SIEs obtained from RMCLS while effectively controlling the level of coloration introduced.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1379–1390},
numpages = {12},
keywords = {dereverberation, multichannel equalization, system identification errors, acoustic signal processing}
}

@article{10.1109/TASLP.2014.2329633,
author = {Schasse, Alexander and Martin, Rainer},
title = {Estimation of Subband Speech Correlations for Noise Reduction via MVDR Processing},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329633},
doi = {10.1109/TASLP.2014.2329633},
abstract = {Recently, it has been proposed to use the minimum-variance distortionless-response (MVDR) approach in single-channel speech enhancement in the short-time frequency domain. By applying optimal FIR filters to each subband signal, these filters reduce additive noise components with less speech distortion compared to conventional approaches. An important ingredient to these filters is the temporal correlation of the speech signals. We derive algorithms to provide a blind estimation of this quantity based on a maximum-likelihood and maximum a-posteriori estimation. To derive proper models for the inter-frame correlation of the speech and noise signals, we investigate their statistics on a large dataset. If the speech correlation is properly estimated, the previously derived subband filters discussed in this work show significantly less speech distortion compared to conventional noise reduction algorithms. Therefore, the focus of the experimental parts of this work lies on the quality and intelligibility of the processed signals. To evaluate the performance of the subband filters in combination with the clean speech inter-frame correlation estimators, we predict the speech quality and intelligibility by objective measures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1355–1365},
numpages = {11},
keywords = {speech quality, Wiener filter, speech intelligibility, speech enhancement, subband filtering, noise reduction}
}

@article{10.1109/TASLP.2014.2320637,
author = {Alinaghi, Atiyeh and Jackson, Philip J. B. and Liu, Qingju and Wang, Wenwu},
title = {Joint Mixing Vector and Binaural Model Based Stereo Source Separation},
year = {2014},
issue_date = {September 2014},
publisher = {IEEE Press},
volume = {22},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2320637},
doi = {10.1109/TASLP.2014.2320637},
abstract = {In this paper the mixing vector (MV) in the statistical mixing model is compared to the binaural cues represented by interaural level and phase differences (ILD and IPD). It is shown that the MV distributions are quite distinct while binaural models overlap when the sources are close to each other. On the other hand, the binaural cues are more robust to high reverberation than MV models. According to this complementary behavior we introduce a new robust algorithm for stereo speech separation which considers both additive and convolutive noise signals to model the MV and binaural cues in parallel and estimate probabilistic time-frequency masks. The contribution of each cue to the final decision is also adjusted by weighting the log-likelihoods of the cues empirically. Furthermore, the permutation problem of the frequency domain blind source separation (BSS) is addressed by initializing the MVs based on binaural cues. Experiments are performed systematically on determined and underdetermined speech mixtures in five rooms with various acoustic properties including anechoic, highly reverberant, and spatially-diffuse noise conditions. The results in terms of signal-to-distortion-ratio (SDR) confirm the benefits of integrating the MV and binaural cues, as compared with two state-of-the-art baseline algorithms which only use MV or the binaural cues.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1434–1448},
numpages = {15},
keywords = {blind source separation, computational auditory scene analysis, reverberation, time-frequency masking}
}

@article{10.1109/TASLP.2014.2327299,
author = {Li, Zhibao and Yiu, Ka Fai Cedric and Nordholm, Sven},
title = {On the Indoor Beamformer Design with Reverberation},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2327299},
doi = {10.1109/TASLP.2014.2327299},
abstract = {Beamforming remains to be an important technique for signal enhancement. For applications in open space, the transfer function describing waves propagation has an explicit expression, which can be employed for beamformer design. However, the function becomes very complex in an indoor environment due to the effects of reverberation. In this paper, this problem is discussed. A method based on the image source method (ISM) is applied to model the room impulse responses (RIRs), which will act as the transfer function between source and sensor. The indoor beamformer design problem is formulated as a minimax optimization problem. We propose and study several optimization models based on the L1-norm to design the beamformer. We found that it is advantageous to separate early and late reverberations in the design process and better designs can be achieved. Several numerical experiments are presented using both simulated data and real recordings to evaluate the proposed methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1225–1235},
numpages = {11},
keywords = {beamformer design, reverberation, signal enhancement, dereverberation}
}

@article{10.1109/TASLP.2014.2329732,
author = {Schmid, Dominic and Enzner, Gerald and Malik, Sarmad and Kolossa, Dorothea and Martin, Rainer},
title = {Variational Bayesian Inference for Multichannel Dereverberation and Noise Reduction},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329732},
doi = {10.1109/TASLP.2014.2329732},
abstract = {Room reverberation and background noise severely degrade the quality of hands-free speech communication systems. In this work, we address the problem of combined speech dereverberation and noise reduction using a variational Bayesian (VB) inference approach. Our method relies on a multichannel state-space model for the acoustic channels that combines frame-based observation equations in the frequency domain with a first-order Markov model to describe the time-varying nature of the room impulse responses. By modeling the channels and the source signal as latent random variables, we formulate a lower bound on the log-likelihood function of the model parameters given the observed microphone signals and iteratively maximize it using an online expectation-maximization approach. Our derivation yields update equations to jointly estimate the channel and source posterior distributions and the remaining model parameters. An inspection of the resulting VB algorithmfor blind equalization and channel identification (VB-BENCH) reveals that the presented framework includes previously proposed methods as special cases. Finally, we evaluate the performance of our approach in terms of speech quality, adaptation times, and speech recognition results to demonstrate its effectiveness for a wide range of reverberation and noise conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1320–1335},
numpages = {16},
keywords = {expectation-maximization algorithm, dereverberation, noise reduction, variational bayes, state-space model}
}

@article{10.1109/TASLP.2014.2328174,
author = {Sch\"{u}ldt, Christian and H\"{a}ndel, Peter},
title = {Decay Rate Estimators and Their Performance for Blind Reverberation Time Estimation},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2328174},
doi = {10.1109/TASLP.2014.2328174},
abstract = {Several approaches for blind estimation of reverberation time have been presented in the literature and decay rate estimation is an integral part of many, if not all, of such approaches. This paper provides both an analytical and experimental comparison, in terms of the bias and variance of three common decay rate estimators; a straight-forward linear regression approach as well as two maximum-likelihood based methods. Situations with and without interfering additive noise are considered. It is shown that the linear regression based approach is unbiased if no smoothing is applied, and that the estimation variance in the absence of noise is constantly about twice that of the maximum-likelihood based methods. It is shown that the methods that do not take possible noise into account suffer from similar estimation bias in the presence of noise. Further, a hybrid method, combining the noise robustness and low computational complexity advantages of the two different maximum-likelihood based methods, is presented.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1274–1284},
numpages = {11},
keywords = {reverberation time, blind estimation, decay rate, maximum-likelihood estimation}
}

@article{10.1109/TASLP.2014.2329188,
author = {Y\'{\i}lmaz, Emre and Gemmeke, Jort Florent and Van Hamme, Hugo},
title = {Noise Robust Exemplar Matching Using Sparse Representations of Speech},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329188},
doi = {10.1109/TASLP.2014.2329188},
abstract = {Performing automatic speech recognition using exemplars (templates) holds the promise to provide a better duration and coarticulation modeling compared to conventional approaches such as hidden Markov models (HMMs). Exemplars are spectrographic representations of speech segments extracted from the training data, each associated with a speech unit, e.g. phones, syllables, half-words or words, and preserve the complete spectro-temporal content of the speech. Conventional exemplar-matching approaches to automatic speech recognition systems, such as those based on dynamic time warping, have typically focused on evaluation in clean conditions. In this paper, we propose a novel noise robust exemplar matching framework for automatic speech recognition. This recognizer approximates noisy speech segments as a weighted sum of speech and noise exemplars and performs recognition by comparing the reconstruction errors of different classes with respect to a divergence measure. We evaluate the system performance in keyword recognition on the small vocabulary track of the 2nd CHiME Challenge and connected digit recognition on the AURORA-2 database. The results show that the proposed system achieves comparable results with state-of-the-art noise robust recognition systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1306–1319},
numpages = {14},
keywords = {sparse representations, reconstruction error, automatic speech recognition, noise robustness, exemplar-based}
}

@article{10.1109/TASLP.2014.2327300,
author = {FanChiang, Yi and Wei, Cheng-Wen and Meng, Yi-Le and Lin, Yu-Wen and Jou, Shyh-Jye and Chang, Tian-Sheuan},
title = {Low Complexity Formant Estimation Adaptive Feedback Cancellation for Hearing Aids Using Pitch Based Processing},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2327300},
doi = {10.1109/TASLP.2014.2327300},
abstract = {This paper proposes a novel algorithm and architecture for the adaptive feedback cancellation (AFC) based on the pitch and the formant information for hearing aid (HA) applications. The proposed method, named as Pitch based Formant Estimation (PFE-AFC), has significantly low complexity compared to Prediction Error Method AFC (PEM-AFC). The proposed PFE-AFC consists of a forward and a backward path processing. The forward path processing includes a low complexity pitch based formant estimator for decorrelation filter coefficients update and a pitch based voice activity detector for speech detection, which facilitates the feedback cancellation filter in the backward path to reduce feedback component and maintain speech quality. From system point of view, the PFE-AFC has low complexity overhead since it is easy to share computation resource with other components in the HA system, such as noise reduction and auditory compensation. In addition, the PFE-AFC is suitable for hardware implementation owing to its regular structure. Complexity evaluations show that the PFE-AFC has four orders lower complexity than the PEM-AFC. Simulation results show that the PFE-AFC and the PEM-AFC can achieve similar PESQ (perceptual evaluation speech quality) and ASG (added stable gain). Moreover, the proposed PFE-AFC can outperform the conventional AFC.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1248–1259},
numpages = {12},
keywords = {hearing aid application, adaptive feedback cancellation, voice activity detection}
}

@article{10.1109/TASLP.2014.2327297,
author = {Conan, Simon and Derrien, Olivier and Aramaki, Mitsuko and Ystad, S\o{}lvi and Kronland-Martinet, Richard},
title = {A Synthesis Model with Intuitive Control Capabilities for Rolling Sounds},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2327297},
doi = {10.1109/TASLP.2014.2327297},
abstract = {This paper presents a physically inspired source-filter model for rolling sound synthesis. The model, which is suitable for real-time implementation, is based on qualitative and quantitative observations obtained from a physics-based model described in the literature. In the first part of the paper, the physics-based model is presented, followed by a perceptual experiment, whose aim is to identify the perceptually relevant information characterizing the rolling interaction. On the basis of this experiment, we hypothesize that the particular pattern of the interaction force is responsible for the perception of a rolling object. A complete analysis-synthesis scheme of this interaction force is then provided, along with a description of the calibration of the proposed source-filter sound synthesis process. Finally, a mapping strategy for intuitive control of the proposed synthesis process (i.e. size and velocity of the rolling object and roughness of the surface) is proposed and validated by a listening test.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1260–1273},
numpages = {14},
keywords = {sound synthesis, audio virtual reality, physics-based model, rolling sounds, sound analysis, source-filter model}
}

@article{10.1109/TASLP.2014.2329190,
author = {Ganapathy, Sriram and Mallidi, Sri Harish and Hermansky, Hynek},
title = {Robust Feature Extraction Using Modulation Filtering of Autoregressive Models},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329190},
doi = {10.1109/TASLP.2014.2329190},
abstract = {Speaker and language recognition in noisy and degraded channel conditions continue to be a challenging problem mainly due to the mismatch between clean training and noisy test conditions. In the presence of noise, the most reliable portions of the signal are the high energy regions which can be used for robust feature extraction. In this paper, we propose a front end processing scheme based on autoregressive (AR) models that represent the high energy regions with good accuracy followed by a modulation filtering process. The AR model of the spectrogram is derived using two separable time and frequency AR transforms. The first AR model (temporal AR model) of the sub-band Hilbert envelopes is derived using frequency domain linear prediction (FDLP). This is followed by a spectral AR model applied on the FDLP envelopes. The output 2-D AR model represents a low-pass modulation filtered spectrogram of the speech signal. The band-pass modulation filtered spectrograms can further be derived by dividing two AR models with different model orders (cut-off frequencies). The modulation filtered spectrograms are converted to cepstral coefficients and are used for a speaker recognition task in noisy and reverberant conditions. Various speaker recognition experiments are performed with clean and noisy versions of the NIST-2010 speaker recognition evaluation (SRE) database using the state-of-the-art speaker recognition system. In these experiments, the proposed front-end analysis provides substantial improvements (relative improvements of up to 25%) compared to baseline techniques. Furthermore, we also illustrate the generalizability of the proposed methods using language identification (LID) experiments on highly degraded high-frequency (HF) radio channels and speech recognition experiments on noisy data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1285–1295},
numpages = {11},
keywords = {feature extraction, modulation filtering, speaker and language recognition, autoregressive modeling}
}

@article{10.1109/TASLP.2014.2329237,
author = {Li, Bo and Sim, Khe Chai},
title = {A Spectral Masking Approach to Noise-Robust Speech Recognition Using Deep Neural Networks},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2329237},
doi = {10.1109/TASLP.2014.2329237},
abstract = {Improving the noise robustness of automatic speech recognition systems has been a challenging task for many years. Recently, it was found that Deep Neural Networks (DNNs) yield large performance gains over conventional GMM-HMM systems, when used in both hybrid and tandem systems. However, they are still far from the level of human expectations especially under adverse environments. Motivated by the separation-prior-to-recognition process of the human auditory system, we propose a robust spectral masking system where power spectral domain masks are predicted using a DNN trained on the same filter-bank features used for acoustic modeling. To further improve performance, Linear Input Network (LIN) adaptation is applied to both the mask estimator and the acoustic model DNNs. Since the estimation of LINs for the mask estimator requires stereo data, which is not available during testing, we proposed using the LINs estimated for the acoustic model DNNs to adapt the mask estimators. Furthermore, we used the same set of weights obtained from pretraining for the input layers of both the mask estimator and the acoustic model DNNs to ensure a better consistency for sharing LINs. Experimental results on benchmark Aurora2 and Aurora4 tasks demonstrated the effectiveness of our system, which yielded Word Error Rates (WERs) of 4.6% and 11.8% respectively. Furthermore, the simple averaging of posteriors from systems with and without spectral masking can further reduce the WERs to 4.3% on Aurora2 and 11.4% on Aurora4.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1296–1305},
numpages = {10},
keywords = {deep neural network, spectral masking, noise robustness}
}

@article{10.1109/TASLP.2014.2327298,
author = {Hawes, Matthew B. and Liu, Wei},
title = {Sparse Array Design for Wideband Beamforming with Reduced Complexity in Tapped Delay-Lines},
year = {2014},
issue_date = {August 2014},
publisher = {IEEE Press},
volume = {22},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2327298},
doi = {10.1109/TASLP.2014.2327298},
abstract = {Sparse wideband array design for sensor location optimization is highly nonlinear and it is traditionally solved by genetic algorithms (GAs) or other similar optimization methods. This is an extremely time-consuming process and an optimum solution is not always guaranteed. In this work, this problem is studied from the viewpoint of compressive sensing (CS). Although there have been CS-based methods proposed for the design of sparse narrowband arrays, its extension to the wideband case is not straightforward, as there are multiple coefficients associated with each sensor and they have to be simultaneously minimized in order to discard the corresponding sensor locations. At first, sensor location optimization for both general wideband beamforming and frequency invariant beamforming is considered. Then, sparsity in the tapped delay-line (TDL) coefficients associated with each sensor is considered in order to reduce the implementation complexity of each TDL. Finally, design of robust wideband arrays against norm-bounded steering vector errors is addressed. Design examples are provided to verify the effectiveness of the proposed methods, with comparisons drawn with a GA-based design method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1236–1247},
numpages = {12},
keywords = {compressive sensing, frequency invariant beamforming, robust beamforming, implementation complexity, wideband beamforming, sparse array}
}

@article{10.1109/TASLP.2014.2320575,
author = {Bao, Guangzhao and Xu, Yangfei and Ye, Zhongfu},
title = {Learning a Discriminative Dictionary for Single-Channel Speech Separation},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2320575},
doi = {10.1109/TASLP.2014.2320575},
abstract = {This paper presents a novel dictionary learning (DL) method to improve the performance of sparsity based single-channel speech separation (SCSS). The conventional approaches regard the sub-dictionaries as independent units and learn sub-dictionaries separately in the short-time Fourier transform (STFT) domain using their corresponding training sets respectively. However, we take the relationship between the sub-dictionaries into account and optimize the sub-dictionaries jointly in the time domain. By satisfying a designed discrimination constraint, a structured dictionary, whose atoms have better correspondences to the speaker labels, is learned so that the sources can be recovered by the corresponding reconstruction after sparse coding. An algorithm, which consists of sparse coding stage and dictionary updating stage, is proposed to deal with this DL optimization problem. Two strategies, i.e., direct learning and adaptive learning, are presented to select the training sets which are used to learn the discriminative dictionary. Experimental results show that the proposed SCSS algorithms have superior performance compared with other tested approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1130–1138},
numpages = {9},
keywords = {dictionary learning, single-channel speech separation, sparsity, discriminative dictionary}
}

@article{10.1109/TASLP.2014.2319159,
author = {Bahari, Mohamad Hasan and Dehak, Najim and Van Hamme, Hugo and Burget, Lukas and Ali, Ahmed M. and Glass, Jim},
title = {Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2319159},
doi = {10.1109/TASLP.2014.2319159},
abstract = {Recent studies show that Gaussian mixture model (GMM) weights carry less, yet complimentary, information to GMM means for language and dialect recognition. However, state-of-the-art language recognition systems usually do not use this information. In this research, a non-negative factor analysis (NFA) approach is developed for GMM weight decomposition and adaptation. This modeling, which is conceptually simple and computationally inexpensive, suggests a new low-dimensional utterance representation method using a factor analysis similar to that of the i-vector framework. The obtained subspace vectors are then applied in conjunction with i-vectors to the language/dialect recognition problem. The suggested approach is evaluated on the NIST 2011 and RATS language recognition evaluation (LRE) corpora and on the QCRI Arabic dialect recognition evaluation (DRE) corpus. The assessment results show that the proposed adaptation method yields more accurate recognition results compared to three conventional weight adaptation approaches, namely maximum likelihood re-estimation, non-negative matrix factorization, and a subspace multinomial model. Experimental results also show that the intermediate-level fusion of i-vectors and NFA subspace vectors improves the performance of the state-of-the-art i-vector framework especially for the case of short utterances.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1117–1129},
numpages = {13},
keywords = {model adaptation, non-negative factor analysis, dialect recognition, language recognition, Gaussian mixture model weight}
}

@article{10.1109/TASLP.2014.2327295,
author = {Shen, Mo and Kawahara, Daisuke and Kurohashi, Sadao},
title = {Dependency Parse Reranking with Rich Subtree Features},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2327295},
doi = {10.1109/TASLP.2014.2327295},
abstract = {In pursuing machine understanding of human language, highly accurate syntactic analysis is a crucial step. In this work, we focus on dependency grammar, which models syntax by encoding transparent predicate-argument structures. Recent advances in dependency parsing have shown that employing higher-order subtree structures in graph-based parsers can substantially improve the parsing accuracy. However, the inefficiency of this approach increases with the order of the subtrees. This work explores a new reranking approach for dependency parsing that can utilize complex subtree representations by applying efficient sub-tree selection methods. We demonstrate the effectiveness of the approach in experiments conducted on the Penn Treebank and the Chinese Treebank. Our system achieves the best performance among known supervised systems evaluated on these datasets, improving the baseline accuracy from 91.88% to 93.42% for English, and from 87.39% to 89.25% for Chinese.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1208–1218},
numpages = {11},
keywords = {dependency parsing, parse reranking, multilingual parsing}
}

@article{10.1109/TASLP.2014.2321482,
author = {Chen, Sin-Horng and Hsieh, Chiao-Hua and Chiang, Chen-Yu and Hsiao, Hsi-Chun and Wang, Yih-Ru and Liao, Yuan-Fu and Yu, Hsiu-Min},
title = {Modeling of Speaking Rate Influences on Mandarin Speech Prosody and Its Application to Speaking Rate-Controlled TTS},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2321482},
doi = {10.1109/TASLP.2014.2321482},
abstract = {A new data-driven approach to building a speaking rate-dependent hierarchical prosodic model (SR-HPM), directly from a large prosody-unlabeled speech database containing utterances of various speaking rates, to describe the influences of speaking rate on Mandarin speech prosody is proposed. It is an extended version of the existing HPM model which contains 12 sub-models to describe various relationships of prosodic-acoustic features of speech signal, linguistic features of the associated text, and prosodic tags representing the prosodic structure of speech. Two main modifications are suggested. One is designing proper normalization functions from the statistics of the whole database to compensate the influences of speaking rate on all prosodic-acoustic features. Another is modifying the HPM training to let its parameters be speaking-rate dependent. Experimental results on a large Mandarin read speech corpus showed that the parameters of the SR-HPM together with these feature normalization functions interpreted the effects of speaking rate onMandarin speech prosody very well. An application of the SR-HPM to design and implement a speaking rate-controlled Mandarin TTS system is demonstrated. The system can generate natural synthetic speech for any given speaking rate in awide range of 3.4-6.8 syllables/sec. Two subjective tests, MOS and preference test, were conducted to compare the proposed system with the popular HTS system. The MOS scores of the proposed system were in the range of 3.58-3.83 for eight different speaking rates, while they were in 3.09-3.43 for HTS. Besides, the proposed system had higher preference scores (49.8%-79.6%) than those (9.8%-30.7%) of HTS. This confirmed the effectiveness of the speaking rate control method of the proposed TTS system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1158–1171},
numpages = {14},
keywords = {speaking rate modeling, speaking rate-controlled TTS, Mandarin prosody modeling}
}

@article{10.1109/TASLP.2014.2321472,
author = {Kelly, Ian J. and Boland, Francis M.},
title = {Detecting Arrivals in Room Impulse Responses with Dynamic Time Warping},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2321472},
doi = {10.1109/TASLP.2014.2321472},
abstract = {The detection of early reflections in room impulse responses is of importance to many algorithms including room geometry inference, mixing time determination and speech dereverberation. The detection of early reflections can be hampered by increasing reflection width, as the direct sound undergoes reflection and by overlapping of the reflections, as the reflection density grows. This paper investigates the use of Dynamic Time Warping upon a direct sound pulse to estimate the temporal distribution of arrivals in room impulse responses. Bounded Dynamic Time Warping is performed, after an initial correlation of the direct sound with the remaining signal, to further refine the arrival's location and duration and to find arrivals which may otherwise not correlate well with the un-warped direct sound due to a change in the reflection's shape. Dynamic Time Warping upon concatenated versions of the direct sound is also used to help find overlapping reflections which may otherwise go undetected. Warping is performed via a set of warp matrices which can be combined together and can also be inverted via a quickly calculated left pseudo-inverse. The algorithm presented is compared to two current methods of arrival detection and is shown to detect a lesser number of spurious arrivals than either of these algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1139–1147},
numpages = {9},
keywords = {early reflections, dynamic time warping, room impulse response}
}

@article{10.1109/TASLP.2014.2324182,
author = {Zhang, Wen and Abhayapala, Thushara D.},
title = {Three Dimensional Sound Field Reproduction Using Multiple Circular Loudspeaker Arrays: Functional Analysis Guided Approach},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2324182},
doi = {10.1109/TASLP.2014.2324182},
abstract = {Three dimensional sound field reproduction based on higher order Ambisonics requires the placement of loudspeakers on a sphere that surrounds the target reproduction region. The deployment of a spherical array is not trivial especially for implementation in real rooms where the placement flexibility is highly desirable. This paper proposes a design of multiple circular loudspeaker arrays for reproducing three dimensional sound fields originating from a limited region of interest. We apply a functional analysis framework to formulate the sound field reproduction problem in a closed form. Secondary source distributions and target sound fields are modeled as two Hilbert spaces and mapped by an integral operator and its adjoint operator, from which a self-adjoint operator is constructed and the singular value decomposition is applied to represent source distributions and sound fields with two sets of interrelated singular functions. We derive the solutions for a circular secondary source arrangement and propose the design of placing multiple circular loudspeaker arrays only over the limited region of interest. Such a design allows for non-spherical and non-uniform loudspeaker placement and thus provides a flexible array arrangement. The reproduction accuracy of the proposed method is verified through numerical simulations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1184–1194},
numpages = {11},
keywords = {spherical harmonics, 3D audio, sound field reproduction, circular loudspeaker arrays, ambisonics}
}

@article{10.1109/TASLP.2014.2327294,
author = {Taseska, Maja and Habets, Emanu\"{e}l A. P.},
title = {Informed Spatial Filtering for Sound Extraction Using Distributed Microphone Arrays},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2327294},
doi = {10.1109/TASLP.2014.2327294},
abstract = {Hands-free acquisition of speech is required in many human-machine interfaces and communication systems. The signals received by integrated microphones contain a desired speech signal, spatially coherent interfering signals, and background noise. In order to enhance the desired speech signal, state-of-the-art techniques apply data-dependent spatial filters which require the second order statistics (SOS) of the desired signal, the interfering signals and the background noise. As the number of sources and the reverberation time increase, the estimation accuracy of the SOS deteriorates, often resulting in insufficient noise and interference reduction. In this paper, a signal extraction framework with distributed microphone arrays is developed. An expectation maximization (EM)-based algorithm detects the number of coherent speech sources and estimates source clusters using time-frequency (TF) bin-wise position estimates. Subsequently, the second order statistics (SOS) are estimated using bin-wise speech presence probability (SPP) and a source probability for each source. Finally, a desired source is extracted using a minimum variance distortionless response (MVDR) filter, a multichannel Wiener filter (MWF) and a parametric multichannel Wiener filter (PMWF). The same framework can be employed for source separation, where a spatial filter is computed for each source considering the remaining sources as interferers. Evaluation using simulated and measured data demonstrates the effectiveness of the framework in estimating the number of sources, clustering, signal enhancement, and source separation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1195–1207},
numpages = {13},
keywords = {spatial filtering, EM algorithm, source extraction, distributed arrays, PSD matrix estimation}
}

@article{10.1109/TASLP.2014.2324175,
author = {Comminiello, Danilo and Scarpiniti, Michele and Azpicueta-Ruiz, Luis A. and Arenas-Garc\'{\i}a, Jer\'{o}nimo and Uncini, Aurelio},
title = {Nonlinear Acoustic Echo Cancellation Based on Sparse Functional Link Representations},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2324175},
doi = {10.1109/TASLP.2014.2324175},
abstract = {Recently, a new class of nonlinear adaptive filtering architectures has been introduced based on the functional link adaptive filter (FLAF) model. Here we focus specifically on the split FLAF (SFLAF) architecture, which separates the adaptation of linear and nonlinear coefficients using two different adaptive filters in parallel. This property makes the SFLAF a well-suited method for problems like nonlinear acoustic echo cancellation (NAEC), in which the separation of filtering tasks brings some performance improvement. Although flexibility is one of the main features of the SFLAF, some problem may occur when the nonlinearity degree of the input signal is not known a priori. This implies a non-optimal choice of the number of coefficients to be adapted in the nonlinear path of the SFLAF. In order to tackle this problem, we propose a proportionate FLAF (PFLAF), which is based on sparse representations of functional links, thus giving less importance to those coefficients that do not actively contribute to the nonlinear modeling. Experimental results show that the proposed PFLAF achieves performance improvement with respect to the SFLAF in several nonlinear scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1172–1183},
numpages = {12},
keywords = {sparse adaptive filters, nonlinear modeling, proportionate adaptive filters, functional link adaptive filters, nonlinear acoustic echo cancellation}
}

@article{10.1109/TASLP.2014.2321475,
author = {Guldenschuh, Markus and De Callafon, Raymond},
title = {Detection of Secondary-Path Irregularities in Active Noise Control Headphones},
year = {2014},
issue_date = {July 2014},
publisher = {IEEE Press},
volume = {22},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2321475},
doi = {10.1109/TASLP.2014.2321475},
abstract = {Headphones with adaptive feedback ANC show a good performance if the secondary-path is well known. The secondary-path however changes considerably if the headphones are lifted. In this paper, it is shown that these changes mainly affect the low frequencies of the secondary-path and that the resulting low-frequency poles of the system cause instabilities. However, the changes in the secondary-path can be detected via low-frequency changes of the adaptive filter. A cost-efficient algorithm in the time-domain is developed to detect and react to these changes in order to keep the ANC system stable. Experimental results show that the algorithm yields the desired ANC performance in the regular use case and still avoids instabilities even during sudden changes in the secondary-path.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1148–1157},
numpages = {10},
keywords = {adaptive filters, noise cancellation, robust stability, active noise reduction, adaptive signal processing, feedback, adaptive control}
}

@article{10.1109/TASLP.2014.2315044,
author = {Nongpiur, R. C.},
title = {Design of Minimax Broadband Beamformers That Are Robust to Microphone Gain, Phase and Position Errors},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2315044},
doi = {10.1109/TASLP.2014.2315044},
abstract = {Broadband beamformers with small-size microphone arrays are known to be highly sensitive to microphone imperfections. A new method for the design of minimax broadband beamformers that are robust to microphone gain, phase, and position errors is proposed. In the method, the maximum variations in the microphone errors are used in formulating a convex optimization problem where the worst-case passband error is minimized under the constraint that the worst-case stopband error is below a prescribed level. To include the microphone imperfections in the optimization problem, we developed a suitable model that incorporates the variations due to the microphone errors and at the same time is efficient to compute. An important advantage of the proposed method is the availability of corresponding worst-case passband - and stopband-error bounds for the beamformer that has been designed; a second advantage is that it does not require the probability distributions of microphone errors. We then describe a two-phase method where the proposed method is used in the first phase to derive the passband and stopband error constraints for solving an optimization problem in the second phase where the white noise gain (WNG) of the beamformer is maximized. In our experiments, we compare beamformers designed using the proposed method, the two-phase method and a modified version of a competing method. Experimental results show that beamformers designed using the proposed method have much better performance than those of the modified competing method and comparable performance with those of the two-phase method; however, unlike the two-phase method, the proposed method provides the additional guarantee that the errors will always lie within the worst-case error bounds.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1013–1022},
numpages = {10},
keywords = {speech enhancement, acoustic beamforming, constrained optimization, broadband beamformer}
}

@article{10.1109/TASLP.2014.2319155,
author = {Reindl, Klaus and Meier, Stefan and Barfuss, Hendrik and Kellermann, Walter},
title = {Minimum Mutual Information-Based Linearly Constrained Broadband Signal Extraction},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2319155},
doi = {10.1109/TASLP.2014.2319155},
abstract = {In this contribution, the problem of broadband acoustic signal extraction is treated as a specific source separation problem, where the desired signal components are to be separated from all remaining undesired components. For this, we exploit the generic TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON) framework. The TRINICON optimization criterion is complemented with linear constraints leading to the Linearly Constrained Minimum Mutual Information (LCMMI) criterion for desired signal extraction. A general linearly constrained update rule for iterative filter optimization is derived, which can efficiently be realized in a novel Minimum Mutual Information (MMI)-Generalized Sidelobe Canceler (GSC). The general treatment of the signal extraction problem using an MMI criterion provides several advantages: Firstly, new insights into the signal extraction problem can be derived by establishing links to both the original GSC and the Multichannel Wiener Filter (MWF). Secondly, by exploiting fundamental properties characteristic for speech and audio signals, complicated and often unreliable Voice Activity Detection (VAD)-based control mechanisms become unnecessary. Thirdly, the overall realization requires only prior information of the desired source position. An evaluation of the MMI-GSC for the double-talk situation with two concurrently active speech sources under reverberant and noisy conditions demonstrates the effectiveness of this novel approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1096–1108},
numpages = {13},
keywords = {wiener filter, linearly constrained minimum mutual information (LCMMI), generalized sidelobe canceler (GSC), interference cancellation, linearly constrained minimum variance (LCMV)}
}

@article{10.1109/TASLP.2014.2319157,
author = {Gangeh, Mehrdad J. and Fewzee, Pouria and Ghodsi, Ali and Kamel, Mohamed S. and Karray, Fakhri},
title = {Multiview Supervised Dictionary Learning in Speech Emotion Recognition},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2319157},
doi = {10.1109/TASLP.2014.2319157},
abstract = {Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt independence criterion (HSIC) has been proposed that learns the dictionary and the corresponding sparse coefficients in a space where the dependency between the data and the corresponding labels is maximized. In this paper, two multiview dictionary learning techniques are proposed based on this HSIC-based SDL. While one of these two techniques learns one dictionary and the corresponding coefficients in the space of fused features in all views, the other learns one dictionary in each view and subsequently fuses the sparse coefficients in the spaces of learned dictionaries. The effectiveness of the proposed multiview learning techniques in using the complementary information of single views is demonstrated in the application of speech emotion recognition (SER). The fully-continuous sub-challenge (FCSC) of the AVEC 2012 dataset is used in two different views: baseline and spectral energy distribution (SED) feature sets. Four dimensional affects, i.e., arousal, expectation, power, and valence are predicted using the proposed multiview methods as the continuous response variables. The results are compared with the single views, AVEC 2012 baseline system, and also other supervised and unsupervised multiview learning approaches in the literature. Using correlation coefficient as the performance measure in predicting the continuous dimensional affects, it is shown that the proposed approach achieves the highest performance among the rivals. The relative performance of the two proposed multiview techniques and their relationship are also discussed. Particularly, it is shown that by providing an additional constraint on the dictionary of one of these approaches, it becomes the same as the other.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1056–1068},
numpages = {13},
keywords = {sparse representation, emotion recognition, supervised learning, multiview representation, dictionary learning}
}

@article{10.1109/TASLP.2014.2318514,
author = {Geiger, J\"{u}rgen T. and Weninger, Felix and Gemmeke, Jort F. and W\"{o}llmer, Martin and Schuller, Bj\"{o}rn and Rigoll, Gerhard},
title = {Memory-Enhanced Neural Networks and NMF for Robust ASR},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2318514},
doi = {10.1109/TASLP.2014.2318514},
abstract = {In this article we address the problem of distant speech recognition for reverberant noisy environments. Speech enhancement methods, e. g., using non-negative matrix factorization (NMF), are succesful in improving the robustness of ASR systems. Furthermore, discriminative training and feature transformations are employed to increase the robustness of traditional systems using Gaussian mixture models (GMM). On the other hand, acoustic models based on deep neural networks (DNN) were recently shown to outperform GMMs. In this work, we combine a state-of-the art GMM system with a deep Long Short-Term Memory (LSTM) recurrent neural network in a double-stream architecture. Such networks use memory cells in the hidden units, enabling them to learn long-range temporal context, and thus increasing the robustness against noise and reverberation. The network is trained to predict frame-wise phoneme estimates, which are converted into observation likelihoods to be used as an acoustic model. It is of particular interest whether the LSTM system is capable of improving a robust state-of-the-art GMM system, which is confirmed in the experimental results. In addition, we investigate the efficiency of NMF for speech enhancement on the front-end side. Experiments are conducted on the medium-vocabulary task of the 2nd 'CHiME' Speech Separation and Recognition Challenge, which includes reverberation and highly variable noise. Experimental results show that the average word error rate of the challenge baseline is reduced by 64% relative. The best challenge entry, a noise-robust state-of-the-art recognition system, is outperformed by 25% relative.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1037–1046},
numpages = {10},
keywords = {long short-term memory, non-negative matrix factorization, noise robust speech recognition, multi-stream recognition}
}

@article{10.1109/TASLP.2014.2318519,
author = {Zhao, Haiquan and Yu, Yi and Gao, Shibin and Zeng, Xiangping and He, Zhengyou},
title = {Memory Proportionate APA with Individual Activation Factors for Acoustic Echo Cancellation},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2318519},
doi = {10.1109/TASLP.2014.2318519},
abstract = {An individual-activation-factor memory proportionate affine projection algorithm (IAF-MPAPA) is proposed for sparse system identification in acoustic echo cancellation (AEC) scenarios. By utilizing an individual activation factor for each adaptive filter coefficient instead of a global activation factor, as in the standard proportionate affine projection algorithm (PAPA), the adaptation energy over the coefficients of the proposed IAF-MPAPA can achieve a better distribution, which leads to an improvement of the convergence performance. Moreover, benefiting from the memory characteristics of the proportionate coefficients, its computational complexity is less than the PAPA and improved PAPA (IPAPA). In the context of AEC and stereophonic AEC (SAEC) for highly sparse impulse responses, simulation results indicate that the proposed IAF-MPAPA outperforms the PAPA, IPAPA, and memory IPAPA (MIPAPA) in terms of the convergence rate and tracking capability when the unknown impulse response suddenly changes.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1047–1055},
numpages = {9},
keywords = {sparse system identification, individual activation factor, adaptive filtering, sparse impulse response, proportionate affine projection algorithm}
}

@article{10.1109/TASLP.2014.2316376,
author = {Venkitaraman, Arun and Seelamantula, Chandra Sekhar},
title = {Binaural Signal Processing Motivated Generalized Analytic Signal Construction and AM-FM Demodulation},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2316376},
doi = {10.1109/TASLP.2014.2316376},
abstract = {Binaural hearing studies show that the auditory system uses the phase-difference information in the auditory stimuli for localization of a sound source. Motivated by this finding, we present a method for demodulation of amplitude-modulated-frequency-modulated (AM-FM) signals using a signal and its arbitrary phase-shifted version. The demodulation is achieved using two allpass filters, whose impulse responses are related through the fractional Hilbert transform (FrHT). The allpass filters are obtained by cosine-modulation of a zero-phase flat-top prototype halfband lowpass filter. The outputs of the filters are combined to construct an analytic signal (AS) from which the AM and FM are estimated. We show that, under certain assumptions on the signal and the filter structures, the AM and FM can be obtained exactly. The AM-FM calculations are based on the quasi-eigenfunction approximation. We then extend the concept to the demodulation of multicomponent signals using uniform and non-uniform cosine-modulated filterbank (FB) structures consisting of flat bandpass filters, including the uniform cosine-modulated, equivalent rectangular bandwidth (ERB), and constant-Q filterbanks. We validate the theoretical calculations by considering application on synthesized AM-FM signals and compare the performance in presence of noise with three other multiband demodulation techniques, namely, the Teager-energy-based approach, the Gabor's AS approach, and the linear transduction filter approach. We also show demodulation results for real signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1023–1036},
numpages = {14},
keywords = {cosine-modulated filterbank, fractional Hilbert transform, demodulation, binaural processing, quasi-eigenfunction approximation, equivalent rectangular bandwidth}
}

@article{10.1109/TASLP.2014.2313917,
author = {Choi, Jae-Hun and Chang, Joon-Hyuk},
title = {Dual-Microphone Voice Activity Detection Technique Based on Two-Step Power Level Difference Ratio},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2313917},
doi = {10.1109/TASLP.2014.2313917},
abstract = {In this paper, we propose a novel dual-microphone voice activity detection (VAD) technique based on the two-step power level difference (PLD) ratio. This technique basically exploits the PLD between the primary microphone and the secondary microphone in a mobile device when the distance between the microphones and the sound source is relatively short. Based on the PLD, we propose the use of the PLD ratio (PLDR) instead of the original PLD to take advantage of the relative difference between the PLD of speech and the PLD of noise. Indeed, the PLDR is obtained by estimating the ratio of the PLD between the input signals and the PLD between the two channel noises during periods without speech. The proposed technique offers a two-step algorithm using the PLDRs including long-term PLDR (LT-PLDR), which characterizes long-term evolution and short-term PLDR (ST-PLDR), which characterizes short-time variation during the first step. LT-PLDR-based and ST-PLDR-based VAD decision are performed using the maximum a posteriori (MAP) probability derived from the model-trust algorithm and combined at the second step to reach a superior VAD decision for both long-term and short-term situations. Extensive experimental results show that the proposed dual-microphone VAD technique outperforms the conventional two-channel VAD method as well as most standardized VAD algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1069–1081},
numpages = {13},
keywords = {dual-microphone, two-step, power level difference ratio, voice activity detection}
}

@article{10.1109/TASLP.2014.2317989,
author = {Alameda-Pineda, Xavier and Horaud, Radu},
title = {A Geometric Approach to Sound Source Localization from Time-Delay Estimates},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2317989},
doi = {10.1109/TASLP.2014.2317989},
abstract = {This paper addresses the problem of sound-source localization from time-delay estimates using arbitrarily-shaped non-coplanar microphone arrays. A novel geometric formulation is proposed, together with a thorough algebraic analysis and a global optimization solver. The proposed model is thoroughly described and evaluated. The geometric analysis, stemming from the direct acoustic propagation model, leads to necessary and sufficient conditions for a set of time delays to correspond to a unique position in the source space. Such sets of time delays are referred to as feasible sets. We formally prove that every feasible set corresponds to exactly one position in the source space, whose value can be recovered using a closed-form localization mapping. Therefore we seek for the optimal feasible set of time delays given, as input, the received microphone signals. This time delay estimation problem is naturally cast into a programming task, constrained by the feasibility conditions derived from the geometric analysis. A global branch-and-bound optimization technique is proposed to solve the problem at hand, hence estimating the best set of feasible time delays and, subsequently, localizing the sound source. Extensive experiments with both simulated and real data are reported; we compare our methodology to four state-of-the-art techniques. This comparison shows that the proposed method combined with the branch-and-bound algorithm outperforms existing methods. These in-depth geometric understanding, practical algorithms, and encouraging results, open several opportunities for future work.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1082–1095},
numpages = {14},
keywords = {constrained multivariate nonlinear programming, time delay estimates, geometric sound source localization}
}

@article{10.1109/TASLP.2014.2313404,
author = {Arora, Vipul and Behera, Laxmidhar},
title = {Musical Source Clustering and Identification in Polyphonic Audio},
year = {2014},
issue_date = {June 2014},
publisher = {IEEE Press},
volume = {22},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2313404},
doi = {10.1109/TASLP.2014.2313404},
abstract = {For music transcription or musical source separation, apart from knowing the multi-F0 contours, it is also important to know which F0 has been played by which instrument. This paper focuses on this aspect, i.e. given the polyphonic audio along with its multiple F0 contours, the proposed system clusters them so as to decide 'which instrument played when.' For the task of identifying the instrument or singers in the polyphonic audio, there are many supervised methods available. But many times individual source audio is not available for training. To address this problem, this paper proposes novel schemes using semi-supervised as well as unsupervised approach to source clustering. The proposed theoretical framework is based on auditory perception theory and is implemented using various tools like probabilistic latent component analysis and graph clustering, while taking into account various perceptual cues for characterizing a source. Experiments have been carried out over a wide variety of datasets - ranging from vocal to instrumental as well as from synthetic to real world music. The proposed scheme significantly outperforms a state of the art unsupervised scheme, which does not make use of the given F0 contours. The proposed semi-supervised approach also performs better than another semi-supervised scheme, which makes use of the given F0 information, in terms of computations as well as accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1003–1012},
numpages = {10},
keywords = {acoustic scene analysis, music information retrieval, polyphonic instrument identification}
}

@article{10.1109/TASLP.2014.2311299,
author = {Lacouture-Parodi, Yesenia and Habets, Emanu\"{e}l A. P. and Chen, Jingdong and Benesty, Jacob},
title = {Multichannel Noise Reduction in the Karhunen-Lo\`{e}ve Expansion Domain},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2311299},
doi = {10.1109/TASLP.2014.2311299},
abstract = {The noise reduction problem is traditionally approached in the time, frequency, or transform domain. Having a signal dependent transform has shown some advantages over the traditional signal independent transform. Recently, the single-channel noise reduction problem in the Karhunen-Lo\`{e}ve expansion (KLE) domain has received special attention. In this paper, the noise reduction problem in the KLE domain is studied from a multichannel perspective. We present a new formulation of the problem, in which inter-channel and inter-mode correlations are optimally exploited. We derive different optimal noise reduction filters and present a set of useful performance measures within this framework. The performance of the different filters is then evaluated through experiments in which not only noise but also competing speech sources are present. It is shown that the proposed multichannel formulation is more robust to competing speech sources than the single-channel approach and that a better compromise between noise reduction and speech distortion can be obtained.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {923–936},
numpages = {14},
keywords = {noise reduction, Karhunen-Lo\`{e}ve expansion (KLE), maximum snr filter, multichannel, tradeoff filter, speech enhancement, minimum variance distortionless response (MVDR) filter, wiener filter}
}

@article{10.1109/TASLP.2014.2311329,
author = {Sadjadi, Seyed Omid and Hansen, John H. L.},
title = {Blind Spectral Weighting for Robust Speaker Identification under Reverberation Mismatch},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2311329},
doi = {10.1109/TASLP.2014.2311329},
abstract = {Room reverberation poses various deleterious effects on performance of automatic speech systems. Speaker identification (SID) performance, in particular, degrades rapidly as reverberation time increases. Reverberation causes two forms of spectro-temporal distortions on speech signals: i) self-masking which is due to early reflections and ii) overlap-masking which is due to late reverberation. Overlap-masking effect of reverberation has been shown to have a greater adverse impact on performance of speech systems. Motivated by this fact, this study proposes a blind spectral weighting (BSW) technique for suppressing the reverberation overlap-masking effect on SID systems. The technique is blind in the sense that prior knowledge of neither the anechoic signal nor the room impulse response is required. Performance of the proposed technique is evaluated on speaker verification tasks under simulated and actual reverberant mismatched conditions. Evaluations are conducted in the context of the conventional GMM-UBM as well as the state-of-the-art i-vector based systems. The GMM-UBM experiments are performed using speech material from a new data corpus well suited for speaker verification experiments under actual reverberant mismatched conditions, entitled MultiRoom8. The i-vector experiments are carried out with microphone (interview and phonecall) data from the NIST SRE 2010 extended evaluation set which are digitally convolved with three different measured room impulse responses extracted from the Aachen impulse response (AIR) database. Experimental results prove that incorporating the proposed blind technique into the standard MFCC feature extraction framework yields significant improvement in SID performance under reverberation mismatch.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {937–945},
numpages = {9},
keywords = {speaker verification, reverberation, mismatch conditions, overlap-masking effect, NIST SRE}
}

@article{10.1109/TASLP.2014.2311925,
author = {Jarrett, Daniel P. and Taseska, Maja and Habets, Emanu\"{e}l A. P. and Naylor, Patrick A.},
title = {Noise Reduction in the Spherical Harmonic Domain Using a Tradeoff Beamformer and Narrowband DOA Estimates},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2311925},
doi = {10.1109/TASLP.2014.2311925},
abstract = {In noise reduction, a common approach is to use a microphone array with a beamformer that combines the individual microphone signals to extract a desired speech signal. The beamformer weights usually depend on the statistics of the noise and desired speech signals, which cannot be directly observed and must be estimated. Estimators based on the speech presence probability (SPP) seek to update the statistics estimates only when desired speech is known to be absent or present. However, they do not normally distinguish between desired and undesired speech sources. In this contribution, an algorithm is proposed to distinguish between these two types of sources using additional spatial information, by estimating a desired speech presence probability based on the combination of a multichannel SPP and a direction of arrival (DOA) based probability. The DOA-based probability is computed using DOA estimates for each time-frequency bin. The estimated statistics are then used to compute the weights of a spherical harmonic domain tradeoff beamformer, which achieves a balance between noise reduction and speech distortion. The performance evaluation demonstrates the effectiveness of the proposed approach at suppressing both background noise and spatially coherent noise. A number of audio examples and sample spectrograms are also provided.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {967–978},
numpages = {12},
keywords = {noise reduction, speech enhancement, speech presence probability, spherical harmonic domain}
}

@article{10.1109/TASLP.2014.2312541,
author = {Z\~{a}o, L. and Coelho, R. and Flandrin, P.},
title = {Speech Enhancement with EMD and Hurst-Based Mode Selection},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2312541},
doi = {10.1109/TASLP.2014.2312541},
abstract = {This paper presents a speech enhancement technique for signals corrupted by nonstationary acoustic noises. The proposed approach applies the empirical mode decomposition (EMD) to the noisy speech signal and obtains a set of intrinsic mode functions (IMF). The main contribution of the proposed procedure is the adoption of the Hurst exponent in the selection of IMFs to reconstruct the speech. This EMD and Hurst-based (EMDH) approach is evaluated in speech enhancement experiments considering environmental acoustic noises with different indices of nonstationarity. The results show that the EMDH improves the segmental signal-to-noise ratio and an overall quality composite measure, encompassing the perceptual evaluation of speech quality (PESQ). Moreover, the short-time objective intelligibility (STOI) measure reinforces the superior performance of EMDH. Finally, the EMDH is also examined in a speaker identification task in noisy conditions. The proposed technique leads to the highest speaker identification rates when compared to the baseline speech enhancement algorithms and also to a multicondition training procedure.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {899–911},
numpages = {13},
keywords = {hurst exponent, empirical mode decomposition, index of nonstationarity, speech enhancement, speaker identification}
}

@article{10.1109/TASLP.2014.2310993,
author = {Lee, Hung-Yi and Shiang, Sz-Rung and Yeh, Ching-Feng and Chen, Yun-Nung and Huang, Yu and Kong, Sheng-Yi and Lee, Lin-Shan},
title = {Spoken Knowledge Organization by Semantic Structuring and a Prototype Course Lecture System for Personalized Learning},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2310993},
doi = {10.1109/TASLP.2014.2310993},
abstract = {It takes very long time to go through a complete online course. Without proper background, it is also difficult to understand retrieved spoken paragraphs. This paper therefore presents a new approach of spoken knowledge organization for course lectures for efficient personalized learning. Automatically extracted key terms are taken as the fundamental elements of the semantics of the course. Key term graph constructed by connecting related key terms forms the backbone of the global semantic structure. Audio/video signals are divided into multi-layer temporal structure including paragraphs, sections and chapters, each of which includes a summary as the local semantic structure. The interconnection between semantic structure and temporal structure together with spoken term detection jointly offer to the learners efficient ways to navigate across the course knowledge with personalized learning paths considering their personal interests, available time and background knowledge. A preliminary prototype system has also been successfully developed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {883–898},
numpages = {16},
keywords = {spoken content retrieval, speech summarization, keyterm extraction, course lectures}
}

@article{10.1109/TASL.2014.2315271,
author = {Rieser, Verena and Lemon, Oliver and Keizer, Simon},
title = {Natural Language Generation as Incremental Planning under Uncertainty: Adaptive Information Presentation for Statistical Dialogue Systems},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2014.2315271},
doi = {10.1109/TASL.2014.2315271},
abstract = {We present and evaluate a novel approach to natural language generation (NLG) in statistical spoken dialogue systems (SDS) using a data-driven statistical optimization framework for incremental information presentation (IP), where there is a trade-off to be solved between presenting "enough" information to the user while keeping the utterances short and understandable. The trained IP model is adaptive to variation from the current generation context (e.g. a user and a non-deterministic sentence planner), and it incrementally adapts the IP policy at the turn level. Reinforcement learning is used to automatically optimize the IP policy with respect to a data-driven objective function. In a case study on presenting restaurant information, we show that an optimized IP strategy trained on Wizard-of-Oz data outperforms a baseline mimicking the wizard behavior in terms of total reward gained. The policy is then also tested with real users, and improves on a conventional hand-coded IP strategy used in a deployed SDS in terms of overall task success. The evaluation found that the trained IP strategy significantly improves dialogue task completion for real users, with up to a 8.2% increase in task success. This methodology also provides new insights into the nature of the IP problem, which has previously been treated as a module following dialogue management with no access to lower-level context features (e.g. from a surface realizer and/or speech synthesizer).},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {979–994},
numpages = {16},
keywords = {natural language user interfaces, reinforcement learning, information presentation, natural language generation}
}

@article{10.1109/TASLP.2014.2311324,
author = {Giacobello, Daniele and Christensen, Mads Gr\ae{}sb\o{}ll and Jensen, Tobias Lindstr\o{}m T. L. and Murthi, Manohar N. and Jensen, S\o{}ren Holdt and Moonen, Marc},
title = {Stable 1-Norm Error Minimization Based Linear Predictors for Speech Modeling},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2311324},
doi = {10.1109/TASLP.2014.2311324},
abstract = {In linear prediction of speech, the 1-norm error minimization criterion has been shown to provide a valid alternative to the 2-norm minimization criterion. However, unlike 2-norm minimization, 1-norm minimization does not guarantee the stability of the corresponding all-pole filter and can generate saturations when this is used to synthesize speech. In this paper, we introduce two new methods to obtain intrinsically stable predictors with the 1-norm minimization. The first method is based on constraining the roots of the predictor to lie within the unit circle by reducing the numerical range of the shift operator associated with the particular prediction problem considered. The second method uses the alternative Cauchy bound to impose a convex constraint on the predictor in the 1-norm error minimization. These methods are compared with two existing methods: the Burg method, based on the 1-norm minimization of the forward and backward prediction error, and the iteratively reweighted 2-norm minimization known to converge to the 1-norm minimization with an appropriate selection of weights. The evaluation gives proof of the effectiveness of the new methods, performing as well as unconstrained 1-norm based linear prediction for modeling and coding of speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {912–922},
numpages = {11},
keywords = {sparse linear prediction, all-pole modeling, linear prediction, autoregressive modeling, convex optimization}
}

@article{10.1109/TASLP.2014.2311319,
author = {Cheer, Jordan and Elliott, Stephen J.},
title = {Comments on "Complete Parallel Narrowband Active Noise Control Systems"},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2311319},
doi = {10.1109/TASLP.2014.2311319},
abstract = {In the above paper Chang and Kuo show that the convergence of parallel narrowband active noise control systems is limited by the interaction between the multiple narrowband adaptive filters. To overcome this problem they propose a new algorithm that provides separate error signals to each of the narrowband adaptive filters, generated by filtering the error signal using bandpass filters tuned to the control frequency of each of the adaptive filters. Although these bandpass filters do not introduce an additional phase shift at the control frequency they do introduce an additional group delay into the secondary path. It is shown in this correspondence that although introducing these bandpass filters allows an increase in the step size it may also limit the convergence speed compared to the conventional algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {995–996},
numpages = {2},
keywords = {adaptive algorithms, active noise control, convergence rate}
}

@article{10.1109/TASLP.2014.2312548,
author = {Zhang, Weibin and Fung, Pascale},
title = {Discriminatively Trained Sparse Inverse Covariance Matrices for Speech Recognition},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2312548},
doi = {10.1109/TASLP.2014.2312548},
abstract = {We propose to use acoustic models with sparse inverse covariance matrices to deal with the well-known over-fitting problem of discriminative training, especially when training data are limited. Compared with traditional diagonal or full covariance models, significant improvement by using sparse inverse covariance matrices has been achieved with maximum likelihood training. In state-of-the-art large vocabulary continuous speech recognition systems, discriminative training is commonly employed to achieve the best system performance. This paper investigates training acoustic models with sparse inverse covariance matrices using one of the most widely used discriminative training criteria-maximum mutual information (MMI). A lasso regularization term is added to the traditional objective function for MMI to automatically sparsify the inverse covariance matrices. The whole training process is then derived by maximizing the new objective function. This is achieved through iteratively maximizing a weak-sense auxiliary function. The final problem is shown to be a convex optimization problem and can be efficiently solved. Experimental results on the published Wall Street Journal and our collected Mandarin data sets show that the acoustic models with sparse inverse covariance matrices consistently outperform the conventional diagonal and full covariance models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {873–882},
numpages = {10},
keywords = {discriminative training, speech recognition, sparse inverse covariance matrix}
}

@article{10.1109/TASLP.2014.2307166,
author = {Osterwise, Christopher and Grant, Steven L.},
title = {On Over-Determined Frequency Domain BSS},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2307166},
doi = {10.1109/TASLP.2014.2307166},
abstract = {This paper introduces two new frequency domain overdetermined blind source separation (BSS) algorithms: Inter-frequency Correlation with Microphone Diversity (ICMD), and ICA with Triggered Principal component analysis (ITP). In the first, we consider different sets of microphones, where in each set the number of microphones and sources are equal. In the second, we extract principal components from an overdetermined mixture to form a determined mixture for separation. Both techniques utilize inter-frequency correlation to align permutations via energy profiles. Both monitor the condition number of an inter-frequency cross-correlation matrix of the normalized de-mixed signals' envelopes to determine if separation has failed for the current ICA input configuration; if so, the input configuration is revised and efficiently realigned to produce a better mixture for separation. The complexities and performances of these algorithms are examined in both simulations and a real-room measurement, with three and five sources. They are also compared to other recent frequency domain BSS algorithms for benchmarking purposes. Results show that generally, ICMD and ITP show similar performance with each other and with one of the benchmarking algorithms. However, ICMD is more computationally efficient.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {956–966},
numpages = {11},
keywords = {blind source separation, independent component analysis, frequency domain, over-determined BSS}
}

@article{10.1109/TASLP.2014.2311322,
author = {Mantena, Gautam and Achanta, Sivanand and Prahallad, Kishore},
title = {Query-by-Example Spoken Term Detection Using Frequency Domain Linear Prediction and Non-Segmental Dynamic Time Warping},
year = {2014},
issue_date = {May 2014},
publisher = {IEEE Press},
volume = {22},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2311322},
doi = {10.1109/TASLP.2014.2311322},
abstract = {The task of query-by-example spoken term detection (QbE-STD) is to find a spoken query within spoken audio data. Current state-of-the-art techniques assume zero prior knowledge about the language of the audio data, and thus explore dynamic time warping (DTW) based techniques for the QbE-STD task. In this paper, we use a variant of DTW based algorithm referred to as non-segmental DTW (NS-DTW), with a computational upper bound of O(mn) and analyze the performance of QbE-STD with Gaussian posteriorgrams obtained from spectral and temporal features of the speech signal. The results show that frequency domain linear prediction cepstral coefficients, which capture the temporal dynamics of the speech signal, can be used as an alternative to traditional spectral parameters such as linear prediction cepstral coefficients, perceptual linear prediction cepstral coefficients and Mel-frequency cepstral coefficients. We also introduce another variant of NS-DTW called fast NS-DTW (FNS-DTW) which uses reduced feature vectors for search. With a reduction factor of α ∈ N, we show that the computational upper bound for FNS-DTW is O(mn/α2) which is faster than NS-DTW.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {946–955},
numpages = {10},
keywords = {query-by-example spoken term detection, frequency domain linear prediction, fast search, dynamic time warping}
}

@article{10.1109/TASLP.2014.2304635,
author = {Crocco, Marco and Trucco, Andrea},
title = {Design of Superdirective Planar Arrays with Sparse Aperiodic Layouts for Processing Broadband Signals via 3-D Beamforming},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2304635},
doi = {10.1109/TASLP.2014.2304635},
abstract = {Planar arrays are used jointly with filter-and-sum beamforming to achieve 3-D spatial discrimination in processing broadband signals. In these systems, the beams are steered in various directions to investigate a given portion of space. The band can be so wide as to require both superdirective performance (to increase directivity at low frequencies) and sparse aperiodic layouts (to avoid grating lobes at high frequencies). We propose an original method to simultaneously optimize the transducer positions and the coefficients of the Finite Impulse Response FIR filters, providing a solution that maintains its validity for whatever steering direction inside a predefined region of interest. A hybrid strategy, analytical for the coefficients and stochastic for the positions, is devised to minimize the beam pattern (BP) energy while maintaining an unaltered signal from the steering direction and controlling the side lobes. The robustness of the superdirectivity is achieved by taking into account the probability density functions for the characteristics of realistic transducers. A distinctive feature of our method is its ability to maintain the computational tractability of the addressed optimization problem by drastically reducing the burden of evaluating the cost function. The obtained results, addressing tens of transducers and several octaves of band, demonstrate the effectiveness of the proposed method in terms of directivity, contrast, and robustness.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {800–815},
numpages = {16},
keywords = {microphone arrays, filter-and-sum beamforming, aperiodic arrays, acoustic imaging, 3-D beamforming, sparse arrays, robust superdirective beamforming, planar arrays}
}

@article{10.1109/TASLP.2014.2305252,
author = {Zapata, Jos\'{e} R. and Davies, Matthew E. P. and G\'{o}mez, Emilia},
title = {Multi-Feature Beat Tracking},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2305252},
doi = {10.1109/TASLP.2014.2305252},
abstract = {A recent trend in the field of beat tracking for musical audio signals has been to explore techniques for measuring the level of agreement and disagreement between a committee of beat tracking algorithms. By using beat tracking evaluation methods to compare all pairwise combinations of beat tracker outputs, it has been shown that selecting the beat tracker which most agrees with the remainder of the committee, on a song-by-song basis, leads to improved performance which surpasses the accuracy of any individual beat tracker used on its own. In this paper we extend this idea towards presenting a single, standalone beat tracking solution which can exploit the benefit ofmutual agreement without the need to run multiple separate beat tracking algorithms. In contrast to existing work, we re-cast the problem as one of selecting between the beat outputs resulting from a single beat tracking model with multiple, diverse input features. Through extended evaluation on a large annotated database, we show that our multi-feature beat tracker can outperform the state of the art, and thereby demonstrate that there is sufficient diversity in input features for beat tracking, without the need for multiple tracking models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {816–825},
numpages = {10},
keywords = {music information retrieval, beat tracking, music signal processing, evaluation}
}

@article{10.1109/TASLP.2014.2304240,
author = {Serizel, Romain and Moonen, Marc and Van Dijk, Bas and Wouters, Jan},
title = {Low-Rank Approximation Based Multichannel Wiener Filter Algorithms for Noise Reduction with Application in Cochlear Implants},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2304240},
doi = {10.1109/TASLP.2014.2304240},
abstract = {This paper presents low-rank approximation based multichannel Wiener filter algorithms for noise reduction in speech plus noise scenarios, with application in

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