text:
are trained discriminatively to further decrease the interference and the distortions of the separated sources. Our experimental results show that using two stages of separation improves the quality of the separated signals by decreasing the interference between the separated sources and distortions compared to separating the sources using a single stage of separation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1773–1783},
numpages = {11}
}

@article{10.1109/TASLP.2017.2711059,
author = {Saki, Fatemeh and Kehtarnavaz, Nasser and Saki, Fatemeh and Kehtarnavaz, Nasser},
title = {Real-Time Unsupervised Classification of Environmental Noise Signals},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2711059},
doi = {10.1109/TASLP.2017.2711059},
abstract = {This paper presents a real-time unsupervised classification of environmental noise signals without knowing the number of noise classes or clusters. A previously developed online frame-based clustering algorithm is modified by adding feature extraction, a smoothing step and a fading step. The developed unsupervised classification or clustering is examined in terms of purity of clusters and normalized mutual information. The results obtained for actual noise signals exhibit the effectiveness of the introduced unsupervised classification in terms of both classification outcome and computational efficiency.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1657–1667},
numpages = {11}
}

@article{10.1109/TASLP.2017.2705346,
author = {Pradhan, Somanath and Patel, Vinal and Somani, Dipen and George, Nithin V. and Pradhan, Somanath and Patel, Vinal and Somani, Dipen and George, Nithin V.},
title = {An Improved Proportionate Delayless Multiband-Structured Subband Adaptive Feedback Canceller for Digital Hearing Aids},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2705346},
doi = {10.1109/TASLP.2017.2705346},
abstract = {Acoustic feedback cancellation is one of the challenging tasks in the design of a behind the ear digital hearing aid. This feedback cancellation is usually achieved by using an adaptive filter. The finite correlation between the desired microphone input signal and the input signal to the loudspeaker results in a biased estimation of the adaptive filter, which may produce disturbances in the hearing aid. Prediction error method PEM has been used in literature to reduce the bias effects. The convergence of a PEM-based feedback canceller can be improved by implementing the adaptive filter in the subband domain. However, a direct subband implementation results in aliasing issues, band-edge problems, and introduces a delay due to analysis and synthesis filters. In order to reduce the aliasing and delay issues, a delayless subband implementation of a PEM-based feedback canceller is designed in this paper. A delayless multiband-structured subband implementation of the feedback canceller is also attempted to further reduce the aliasing and band-edge effects. This implementation aims at having all the subbands collectively updating the fullband adaptive filter, without the need for a subband to fullband weight conversion and offers improved feedback cancellation at reduced computational load in comparison with a delayless subband implementation of a PEM-based feedback canceller. In addition, an attempt has been made to further improve the convergence behavior by using an improved proportionate learning scheme. The improved convergence offered by the proposed scheme is evident from the simulation study. The improvement has been further quantified using a perceptual evaluation of speech quality and the proposed approach has been shown to provide enhanced speech quality.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1633–1643},
numpages = {11}
}

@article{10.1109/TASLP.2017.2699424,
author = {Stevens, Francis and Murphy, Damian T and Savioja, Lauri and Valimaki, Vesa and Stevens, Francis and Murphy, Damian T. and Savioja, Lauri and Valimaki, Vesa},
title = {Modeling Sparsely Reflecting Outdoor Acoustic Scenes Using the Waveguide Web},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2699424},
doi = {10.1109/TASLP.2017.2699424},
abstract = {Computer games and virtual reality require digital reverberation algorithms, which can simulate a broad range of acoustic spaces, including locations in the open air. Additionally, the detailed simulation of environmental sound is an area of significant interest due to the propagation of noise pollution over distances and its related impact on well-being, particularly in urban spaces. This paper introduces the waveguide web digital reverberator design for modeling the acoustics of sparsely reflecting outdoor environments; a design that is, in part, an extension of the scattering delay network reverberator. The design of the algorithm is based on a set of digital waveguides connected by scattering junctions at nodes that represent the reflection points of the environment under study. The structure of the proposed reverberator allows for accurate reproduction of reflections between discrete reflection points. Approximation errors are caused when the assumption of point-like nodes does not hold true. Three example cases are presented comparing waveguide web simulated impulse responses for a traditional shoebox room, a forest scenario, and an urban courtyard, with impulse responses created using other simulation methods or from real-world measurements. The waveguide web algorithm can better enable the acoustic simulation of outdoor spaces and so contribute toward sound design for virtual reality applications, gaming, and auralization, with a particular focus on acoustic design for the urban environment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1566–1578},
numpages = {13}
}

@article{10.1109/TASLP.2017.2702385,
author = {Elshamy, Samy and Madhu, Nilesh and Tirry, Wouter and Fingscheidt, Tim and Elshamy, Samy and Madhu, Nilesh and Tirry, Wouter and Fingscheidt, Tim},
title = {Instantaneous A Priori SNR Estimation by Cepstral Excitation Manipulation},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2702385},
doi = {10.1109/TASLP.2017.2702385},
abstract = {As the a priori signal-to-noise ratio SNR contains crucial information about a signal's mixture of speech and noise, its estimation is subject to steady research. In this paper, we introduce a novel a priori SNR estimator based on synthesizing an idealized excitation signal in the cepstral domain. Our approach utilizes a source-filter decomposition in combination with a cepstral excitation manipulation in order to recreate an idealized excitation, which is subsequently shaped by an immanent envelope. In contrast to the well-known decision-directed approach by Ephraim and Malah, an instantaneous estimate is obtained, which is less prone to sudden acoustic environmental changes and musical noise. Additionally, the proposed estimator is able to preserve weak harmonic structures resulting in a spectrum that is more full-bodied. We present both a speaker-independent and a speaker-dependent variant of the new a priori SNR estimator, both showing more than 2 dB ΔSNR improvement versus state of the art, without any significant increase in speech distortion.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1592–1605},
numpages = {14}
}

@article{10.1109/TASLP.2017.2696308,
author = {Schwartz, Ofer and Gannot, Sharon and Habets, Emanuel A. P. and Schwartz, Ofer and Gannot, Sharon and Habets, Emanuel A. P.},
title = {Cram\'{e}r-Rao Bound Analysis of Reverberation Level Estimators for Dereverberation and Noise Reduction},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2696308},
doi = {10.1109/TASLP.2017.2696308},
abstract = {The reverberation power spectral density PSD is often required for dereverberation and noise reduction algorithms. In this work, we compare two maximum likelihood ML estimators of the reverberation PSD in a noisy environment. In the first estimator, the direct path is first blocked. Then, the ML criterion for estimating the reverberation PSD is stated according to the probability density function of the blocking matrix BM outputs. In the second estimator, the speech component is not blocked. Instead, the ML criterion for estimating the speech and reverberation PSD is stated according to the probability density function of the microphone signals. To compare the expected mean square error MSE between the two ML estimators of the reverberation PSD, the Cram\'{e}r-Rao Bounds CRBs for the two ML estimators are derived. We show that the CRB for the joint reverberation and speech PSD estimator is lower than the CRB for estimating the reverberation PSD from the BM outputs. Experimental results show that the MSE of the two estimators indeed obeys the CRB curves. Experimental results of multimicrophone dereverberation and noise reduction algorithm show the benefits of using the ML estimators in comparison with another baseline estimators.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1680–1693},
numpages = {14}
}

@article{10.1109/TASLP.2017.2678164,
author = {Kaushik, Lakshmish and Sangwan, Abhijeet and Hansen, John H. L. and Kaushik, Lakshmish and Sangwan, Abhijeet and Hansen, John H. L.},
title = {Automatic Sentiment Detection in Naturalistic Audio},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2678164},
doi = {10.1109/TASLP.2017.2678164},
abstract = {Audio sentiment analysis using automatic speech recognition is an emerging research area where opinion or sentiment exhibited by a speaker is detected from natural audio. It is relatively underexplored when compared to text based sentiment detection. Extracting speaker sentiment from natural audio sources is a challenging problem. Generic methods for sentiment extraction generally use transcripts from a speech recognition system, and process the transcript using text-based sentiment classifiers. In this study, we show that this baseline system is suboptimal for audio sentiment extraction. Alternatively, new architecture using keyword spotting KWS is proposed for sentiment detection. In the new architecture, a text-based sentiment classifier is utilized to automatically determine the most useful and discriminative sentiment-bearing keyword terms, which are then used as a term list for KWS. In order to obtain a compact yet discriminative sentiment term list, iterative feature optimization for maximum entropy sentiment model is proposed to reduce model complexity while maintaining effective classification accuracy. A new hybrid ME-KWS joint scoring methodology is developed to model both text and audio based parameters in a single integrated formulation. For evaluation, two new databases are developed for audio based sentiment detection, namely, YouTube sentiment database and another newly developed corpus called UT-Opinion Opinion audio archive. These databases contain naturalistic opinionated audio collected in real-world conditions. The proposed solution is evaluated on audio obtained from videos in youtube.com and UT-Opinion corpus. Our experimental results show that the proposed KWS based system significantly outperforms the traditional ASR architecture in detecting sentiment for challenging practical tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1668–1679},
numpages = {12}
}

@article{10.1109/TASLP.2017.2714839,
author = {Chien, Yu-Ren and Mehta, Daryush D. and Guenason, Jon and Zanartu, Matias and Quatieri, Thomas F. and Yu-Ren Chien and Mehta, Daryush D. and Guenason, Jon and Zanartu, Matias and Quatieri, Thomas F.},
title = {Evaluation of Glottal Inverse Filtering Algorithms Using a Physiologically Based Articulatory Speech Synthesizer},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2714839},
doi = {10.1109/TASLP.2017.2714839},
abstract = {Glottal inverse filtering aims to estimate the glottal airflow signal from a speech signal for applications such as speaker recognition and clinical voice assessment. Nonetheless, evaluation of inverse filtering algorithms has been challenging due to the practical difficulties of directly measuring glottal airflow. Apart from this, it is acknowledged that the performance of many methods degrade in voice conditions that are of great interest, such as breathiness, high pitch, soft voice, and running speech. This paper presents a comprehensive, objective, and comparative evaluation of state-of-the-art inverse filtering algorithms that takes advantage of speech and glottal airflow signals generated by a physiological speech synthesizer. The synthesizer provides a physics-based simulation of the voice production process and thus an adequate test bed for revealing the temporal and spectral performance characteristics of each algorithm. Included in the synthetic data are continuous speech utterances and sustained vowels, which are produced with multiple voice qualities pressed, slightly pressed, modal, slightly breathy, and breathy, fundamental frequencies, and subglottal pressures to simulate the natural variations in real speech. In evaluating the accuracy of a glottal flow estimate, multiple error measures are used, including an error in the estimated signal that measures overall waveform deviation, as well as an error in each of several clinically relevant features extracted from the glottal flow estimate. Waveform errors calculated from glottal flow estimation experiments exhibited mean values around 30% for sustained vowels, and around 40% for continuous speech, of the amplitude of true glottal flow derivative. Closed-phase approaches showed remarkable stability across different voice qualities and subglottal pressures. The algorithms of choice, as suggested by significance tests, are closed-phase covariance analysis for the analysis of sustained vowels, and sparse linear prediction for the analysis of continuous speech. Results of data subset analysis suggest that analysis of close rounded vowels is an additional challenge in glottal flow estimation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1718–1730},
numpages = {13}
}

@article{10.1109/TASLP.2017.2714424,
author = {Khademi, Seyran and Hendriks, Richard C. and Kleijn, W. Bastiaan and Khademi, Seyran and Hendriks, Richard C. and Kleijn, W. Bastiaan},
title = {Intelligibility Enhancement Based on Mutual Information},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2714424},
doi = {10.1109/TASLP.2017.2714424},
abstract = {Speech intelligibility enhancement is considered for multiple-microphone acquisition and single loudspeaker rendering. This is based on the mutual information measured between the message spoken at far-end environment and the message perceived by a listener at near-end. We prove that the joint optimal processing can be decomposed into far-end and near-end processing. The former is a minimum variance distortionless response beamformer that reduces the noise in the talker environment and the latter is a post-filter that redistributes the power over the frequency bands. Disjoint processing is optimal provided that the post-filtering operation is aware of the residual noise from the beamforming operation. Our results show that both processing steps are necessary for the effective conveyance of a message and, importantly, that the second step must be aware of the remaining noise from the beamforming operation in the first step. In addition, we study the use of the mutual information applied on the perceptually more relevant powers per critical band.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1694–1708},
numpages = {15}
}

@article{10.1109/TASLP.2017.2703650,
author = {Pang, Cheng and Liu, Hong and Zhang, Jie and Li, Xiaofei and Cheng Pang and Hong Liu and Jie Zhang and Xiaofei Li},
title = {Binaural Sound Localization Based on Reverberation Weighting and Generalized Parametric Mapping},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2703650},
doi = {10.1109/TASLP.2017.2703650},
abstract = {Binaural sound source localization is an important technique for speech enhancement, video conferencing, and human-robot interaction, etc. However, in realistic scenarios, the reverberation and environmental noise would degrade the precision of sound direction estimation. Therefore, reliable sound localization is essential to practical applications. To deal with these disturbances, this paper presents a novel binaural sound source localization approach based on reverberation weighting and generalized parametric mapping. First, the reverberation weighting as a preprocessing stage, is used to separately suppress the early and late reverberation, while preserving interaural cues. Then, two binaural cues, i.e., interaural time and intensity differences, are extracted from the frequency-domain representations of dereverberated binaural signals for the online localization. Their corresponding templates are established using the training data. Furthermore, the generalized parametric mapping is proposed to build a generalized parametric model for describing relationships between azimuth and binaural cues analytically. Finally, a two-step sound localization process is introduced to refine azimuth estimation based on the generalized parametric model and template matching. Experiments in both simulated and real scenarios validate that the proposed method can achieve better localization performance compared to state-of-the-art methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1618–1632},
numpages = {15}
}

@article{10.1109/TASLP.2017.2700945,
author = {Olivieri, Ferdinando and Fazi, Filippo Maria and Fontana, Simone and Menzies, Dylan and Nelson, Philip Arthur and Olivieri, Ferdinando and Fazi, Filippo Maria and Fontana, Simone and Menzies, Dylan and Nelson, Philip Arthur},
title = {Generation of Private Sound With a Circular Loudspeaker Array and the Weighted Pressure Matching Method},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2700945},
doi = {10.1109/TASLP.2017.2700945},
abstract = {In this paper, we propose a system for private sound which is based on the weighted pressure matching method WPMM. The aim is to design the input signals to an array of loudspeakers that allow for the synthesis of a target field defined with large amplitude variations between the so-called dark points and the listener's position. The system enables listeners to control the trade-off between directivity performance and the accuracy of reproduction of the target field at the listening position when the input energy to the array is limited. This is achieved by calculating the WPMM weight in the dark zone based on a constraint on the characteristics of the sound field in the listening zone. The system is validated for a number of predefined use-case scenarios. The results of the experiments in an anechoic environment with a circular array prototype show that listeners can control the performance trade-off in a wide frequency range. In the second part of the paper, algorithms are presented for the fast update of the input signals when the user selects a new value of the performance constraint.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1579–1591},
numpages = {13}
}

@article{10.1109/TASLP.2017.2709909,
author = {Drgas, Szymon and Virtanen, Tuomas and Lucke, Jorg and Hurmalainen, Antti and Drgas, Szymon and Virtanen, Tuomas and Lucke, Jorg and Hurmalainen, Antti},
title = {Binary Non-Negative Matrix Deconvolution for Audio Dictionary Learning},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2709909},
doi = {10.1109/TASLP.2017.2709909},
abstract = {In this study, we propose an unsupervised method for dictionary learning in audio signals. The new method, called binary nonnegative matrix deconvolution BNMD, is developed and used to discover patterns from magnitude-scale spectrograms. The BNMD models an audio spectrogram as a sum of delayed patterns having binary gains activations. Only small subsets of patterns can be active for a given spectrogram excerpt. The proposed method was applied to speaker identification and separation tasks. The experimental results show that dictionaries obtained by the BNMD bring much higher speaker identification accuracies averaged over a range of SNRs from -6 dB to 9 dB 91.3% than the NMD-based dictionaries 37.8-75.4%. The BNMD also gives a benefit over dictionaries obtained using vector quantization 87.8%. For bigger dictionaries the difference between the BNMD and the vector quantization VQ is getting smaller. For the speech separation task the BNMD dictionary gave a slight improvement over the VQ.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1644–1656},
numpages = {13}
}

@article{10.1109/TASLP.2017.2705280,
author = {Hatano, Yuta and Shi, Chuang and Kajikawa, Yoshinobu and Hatano, Yuta and Chuang Shi and Kajikawa, Yoshinobu},
title = {Compensation for Nonlinear Distortion of the Frequency Modulation-Based Parametric Array Loudspeaker},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2705280},
doi = {10.1109/TASLP.2017.2705280},
abstract = {The parametric array loudspeaker PAL modulates the audio signal on an ultrasonic carrier. When the modulated signal is transmitted in air, an audio beam is created based on the nonlinear acoustic principle. Each modulation method has advantages and disadvantages. The frequency modulation FM is favorable for its low cost and high volume, but the tradeoff is its complicated nonlinear distortion, which is difficult to be reduced. In this paper, the Volterra filter is adopted to model the nonlinearity of the FM-based PAL. A novel complex inverse system is devised to effectively reduce the nonlinear distortion. Three practical aspects are addressed. First, the computational complexity of the Volterra filter is reduced by the parallel cascade structure with almost no compromise to the model accuracy. Second, Volterra filters are identified at discrete input levels to treat the nonlinearity that keeps changing with the time-varying audio input. Third, when the input level is high, a separation approach is proposed to refine the identified Volterra filters, which eventually improves the performance of the proposed inverse system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1709–1717},
numpages = {9}
}

@article{10.1109/TASLP.2017.2703165,
author = {Alku, Paavo and Saeidi, Rahim and Alku, Paavo and Saeidi, Rahim},
title = {The Linear Predictive Modeling of Speech From Higher-Lag Autocorrelation Coefficients Applied to Noise-Robust Speaker Recognition},
year = {2017},
issue_date = {August 2017},
publisher = {IEEE Press},
volume = {25},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2703165},
doi = {10.1109/TASLP.2017.2703165},
abstract = {A linear predictive spectral estimation method based on higher-lag autocorrelation coefficients is proposed for the noise-robust feature extraction from speech. The method, called higher-lag linear prediction, is derived from a signal prediction model that is optimized in the mean square sense using a cost function that has two prediction error terms, the first of which is similar to that of conventional linear prediction and the second of which is a delayed version introducing an integer delay of M samples. This basic form is developed further into the combined higher-lag linear prediction CHLLP model by simultaneously taking advantage of the zero-lag and higher-lag predictions. The CHLLP model was used in the computation of mel-frequency cepstral coefficients and compared with several reference feature extraction methods in speaker recognition. The experiments were conducted by using a modern i-vector-based system. Noise-corruption was done using both additive car, babble, and factory noise in different signal-to-noise ratio conditions as well as speech recordings from real noisy conditions. The results indicate that CHLLP outperformed the reference feature extraction methods in almost all the comparisons in the noise-corrupted conditions and the performance of CHLLP was only slightly inferior to the nonparametric FFT-based spectral modeling in the clean condition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1606–1617},
numpages = {12}
}

@article{10.1109/TASLP.2017.2698723,
author = {Lu, Liang and Renals, Steve and Liang Lu and Renals, Steve},
title = {Small-Footprint Highway Deep Neural Networks for Speech Recognition},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2698723},
doi = {10.1109/TASLP.2017.2698723},
abstract = {State-of-the-art speech recognition systems typically employ neural network acoustic models. However, compared to Gaussian mixture models, deep neural network DNN based acoustic models often have many more model parameters, making it challenging for them to be deployed on resource-constrained platforms, such as mobile devices. In this paper, we study the application of the recently proposed highway deep neural network HDNN for training small-footprint acoustic models. HDNNs are a depth-gated feedforward neural network, which include two types of gate functions to facilitate the information flow through different layers. Our study demonstrates that HDNNs are more compact than regular DNNs for acoustic modeling, i.e., they can achieve comparable recognition accuracy with many fewer model parameters. Furthermore, HDNNs are more controllable than DNNs: The gate functions of an HDNN can control the behavior of the whole network using a very small number of model parameters. Finally, we show that HDNNs are more adaptable than DNNs. For example, simply updating the gate functions using adaptation data can result in considerable gains in accuracy. We demonstrate these aspects by experiments using the publicly available AMI corpus, which has around 80 h of training data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1502–1511},
numpages = {10}
}

@article{10.1109/TASLP.2017.2695718,
author = {Fakhry, Mahmoud and Svaizer, Piergiorgio and Omologo, Maurizio and Fakhry, Mahmoud and Svaizer, Piergiorgio and Omologo, Maurizio},
title = {Audio Source Separation in Reverberant Environments Using $\beta$-Divergence-Based Nonnegative Factorization},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2695718},
doi = {10.1109/TASLP.2017.2695718},
abstract = {In Gaussian model-based multichannel audio source separation, the likelihood of observed mixtures of source signals is parametrized by source spectral variances and by associated spatial covariance matrices. These parameters are estimated by maximizing the likelihood through an expectation-maximization algorithm and used to separate the signals by means of multichannel Wiener filtering. We propose to estimate these parameters by applying nonnegative factorization based on prior information on source variances. In the nonnegative factorization, spectral basis matrices can be defined as the prior information. The matrices can be either extracted or indirectly made available through a redundant library that is trained in advance. In a separate step, applying nonnegative tensor factorization, two algorithms are proposed in order to either extract or detect the basis matrices that best represent the power spectra of the source signals in the observed mixtures. The factorization is achieved by minimizing the β-divergence through multiplicative update rules. The sparsity of factorization can be controlled by tuning the value of β. Experiments show that sparsity, rather than the value assigned to β in the training, is crucial in order to increase the separation performance. The proposed method was evaluated in several mixing conditions. It provides better separation quality with respect to other comparable algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1462–1476},
numpages = {15}
}

@article{10.1109/TASLP.2017.2694708,
author = {Zeinali, Hossein and Sameti, Hossein and Burget, Lukas and Zeinali, Hossein and Sameti, Hossein and Burget, Lukas},
title = {HMM-Based Phrase-Independent i-Vector Extractor for Text-Dependent Speaker Verification},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2694708},
doi = {10.1109/TASLP.2017.2694708},
abstract = {The low-dimensional i-vector representation of speech segments is used in the state-of-the-art text-independent speaker verification systems. However, i-vectors were deemed unsuitable for the text-dependent task, where simpler and older speaker recognition approaches were found more effective. In this work, we propose a straightforward hidden Markov model HMM based extension of the i-vector approach, which allows i-vectors to be successfully applied to text-dependent speaker verification. In our approach, the Universal Background Model UBM for training phrase-independent i-vector extractor is based on a set of monophone HMMs instead of the standard Gaussian Mixture Model GMM. To compensate for the channel variability, we propose to precondition i-vectors using a regularized variant of within-class covariance normalization, which can be robustly estimated in a phrase-dependent fashion on the small datasets available for the text-dependent task. The verification scores are cosine similarities between the i-vectors normalized using phrase-dependent s-norm. The experimental results on RSR2015 and RedDots databases confirm the effectiveness of the proposed approach, especially in rejecting test utterances with a wrong phrase. A simple MFCC based i-vector/HMM system performs competitively when compared to very computationally expensive DNN-based approaches or the conventional relevance MAP GMM-UBM, which does not allow for compact speaker representations. To our knowledge, this paper presents the best published results obtained with a single system on both RSR2015 and RedDots dataset.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1421–1435},
numpages = {15}
}

@article{10.1109/TASLP.2017.2700540,
author = {Wang, Yannan and Du, Jun and Dai, Li-Rong and Lee, Chin-Hui and Yannan Wang and Jun Du and Li-Rong Dai and Chin-Hui Lee},
title = {A Gender Mixture Detection Approach to Unsupervised Single-Channel Speech Separation Based on Deep Neural Networks},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2700540},
doi = {10.1109/TASLP.2017.2700540},
abstract = {We propose an unsupervised speech separation framework for mixtures of two unseen speakers in a single-channel setting based on deep neural networks DNNs. We rely on a key assumption that two speakers could be well segregated if they are not too similar to each other. A dissimilarity measure between two speakers is first proposed to characterize the separation ability between competing speakers. We then show that speakers with the same or different genders can often be separated if two speaker clusters, with large enough distances between them, for each gender group could be established, resulting in four speaker clusters. Next, a DNN-based gender mixture detection algorithm is proposed to determine whether the two speakers in the mixture are females, males, or from different genders. This detector is based on a newly proposed DNN architecture with four outputs, two of them representing the female speaker clusters and the other two characterizing the male groups. Finally, we propose to construct three independent speech separation DNN systems, one for each of the female-female, male-male, and female-male mixture situations. Each DNN gives dual outputs, one representing the target speaker group and the other characterizing the interfering speaker cluster. Trained and tested on the speech separation challenge corpus, our experimental results indicate that the proposed DNN-based approach achieves large performance gains over the state-of-the-art unsupervised techniques without using any specific knowledge about the mixed target and interfering speakers being segregated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1535–1546},
numpages = {12}
}

@article{10.1109/TASLP.2017.2693565,
author = {Chen, Yu-An and Wang, Ju-Chiang and Yang, Yi-Hsuan and Chen, Homer H. and Yu-An Chen and Ju-Chiang Wang and Yi-Hsuan Yang and Chen, Homer H.},
title = {Component Tying for Mixture Model Adaptation in Personalization of Music Emotion Recognition},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2693565},
doi = {10.1109/TASLP.2017.2693565},
abstract = {Personalizing a music emotion recognition model is needed because the perception of music emotion is highly subjective, but it is a time-consuming process. In this paper, we consider how to expedite the personalization process that begins with a general model trained offline using a general user base and progressively adapts the model to a music listener using the emotion annotations of the listener. Specifically, we focus on reducing the number of user annotations needed for the personalization. We investigate and evaluate four component tying methods: single group tying, quadrantwise tying, hierarchical tying, and random tying. These methods aim to exploit the available annotations by identifying related model parameters on-the-fly and updating them jointly. In the evaluation, we use the AMG1608 dataset, which contains the clip-level valence-arousal emotion ratings of 1608 30-s music clips annotated by 665 listeners. Also, we use the acoustic emotion Gaussians model as the general model that uses a mixture of Gaussian components to learn the mapping between the acoustic feature space and the emotion space. The results show that the model adaptation with component tying requires only 10-20 personal annotations to obtain the same level of prediction accuracy as the baseline model adaptation method that uses 50 personal annotations without component tying.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1409–1420},
numpages = {12}
}

@article{10.1109/TASLP.2017.2694699,
author = {Korpusik, Mandy and Glass, James and Korpusik, Mandy and Glass, James},
title = {Spoken Language Understanding for a Nutrition Dialogue System},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2694699},
doi = {10.1109/TASLP.2017.2694699},
abstract = {Food logging is recommended by dieticians for prevention and treatment of obesity, but currently available mobile applications for diet tracking are often too difficult and time-consuming for patients to use regularly. For this reason, we propose a novel approach to food journaling that uses speech and language understanding technology in order to enable efficient self-assessment of energy and nutrient consumption. This paper presents ongoing language understanding experiments conducted as part of a larger effort to create a nutrition dialogue system that automatically extracts food concepts from a user's spoken meal description. We first summarize the data collection and annotation of food descriptions performed via Amazon Mechanical Turk AMT, for both a written corpus and spoken data from an in-domain speech recognizer. We show that the addition of word vector features improves conditional random field CRF performance for semantic tagging of food concepts, achieving an average F1 test score of 92.4 on written data; we also demonstrate that a convolutional neural network CNN with no hand-crafted features outperforms the best CRF on spoken data, achieving an F1 test score of 91.3. We illustrate two methods for associating foods with properties: segmenting meal descriptions with a CRF, and a complementary method that directly predicts associations with a feed-forward neural network. Finally, we conduct an end-to-end system evaluation through an AMT user study with worker ratings of 83% semantic tagging accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1450–1461},
numpages = {12}
}

@article{10.1109/TASLP.2017.2699326,
author = {Kodrasi, Ina and Doclo, Simon and Kodrasi, Ina and Doclo, Simon},
title = {Signal-Dependent Penalty Functions for Robust Acoustic Multi-Channel Equalization},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2699326},
doi = {10.1109/TASLP.2017.2699326},
abstract = {Acoustic multi-channel equalization techniques, which aim to achieve dereverberation by reshaping the room impulse responses RIRs between the source and the microphone array, are known to be highly sensitive to RIR perturbations. In order to increase the robustness against RIR perturbations, several signal-independent methods have been proposed, which only rely on the available perturbed RIRs and do not incorporate any knowledge about the output signal. This paper presents a novel signal-dependent method to increase the robustness of equalization techniques by enforcing the output signal to exhibit spectrotemporal characteristics of a clean speech signal. Motivated by the sparse nature of clean speech, we propose to extend the cost function of state-of-the-art least squares equalization techniques, i.e., the multiple-input/output inverse theorem MINT, relaxed multi-channel least squares RMCLS, and partial multi-channel equalization based on MINT PMINT, with a signal-dependent penalty function promoting sparsity of the output signal in the short-time Fourier transform domain. Three conventionally used sparsity-promoting penalty functions are investigated, i.e., the l0-norm, the l1-norm, and the weighted l1-norm, and the sparsitypromoting reshaping filters are iteratively computed using the alternating direction method of multipliers. Simulation results for several acoustic systems and RIR perturbations demonstrate that incorporating sparsity-promoting penalty functions significantly increases the robustness of MINT, RMCLS, and PMINT, with the weighted l1-norm typically outperforming the l0-norm and the l1-norm. Furthermore, it is shown that the weighted l1-norm sparsity-promoting PMINT technique outperforms the other sparsity-promoting techniques in terms of perceptual speech quality. Finally, it is shown that the signal-dependent weighted l1-norm sparsity-promoting PMINT technique yields a similar or better dereverberation performance than the signal-independent regularized PMINT technique, confirming the advantage of using signal-dependent penalty functions for robust dereverberation filter design.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1512–1525},
numpages = {14}
}

@article{10.1109/TASLP.2017.2696310,
author = {Laufer-Goldshtein, Bracha and Talmon, Ronen and Gannot, Sharon and Laufer-Goldshtein, Bracha and Talmon, Ronen and Gannot, Sharon},
title = {Semi-Supervised Source Localization on Multiple Manifolds With Distributed Microphones},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2696310},
doi = {10.1109/TASLP.2017.2696310},
abstract = {The problem of single-source localization with ad hoc microphone networks in noisy and reverberant enclosures is addressed in this paper. A training set is formed by prerecorded measurements collected in advance and consists of a limited number of labelled measurements, attached with corresponding positions, and a larger number of unlabelled measurements from unknown locations. Further information about the enclosure characteristics or the microphone positions is not required. We propose a Bayesian inference approach for estimating a function that maps measurement-based features to the corresponding positions. The signals measured by the microphones represent different viewpoints, which are combined in a unified statistical framework. For this purpose, the mapping function is modelled by a Gaussian process with a covariance function that encapsulates both the connections between pairs of microphones and the relations among the samples in the training set. The parameters of the process are estimated by optimizing a maximum likelihood criterion. In addition, a recursive adaptation mechanism is derived, where the new streaming measurements are used to update the model. Performance is demonstrated for both simulated data and real-life recordings in a variety of reverberation and noise levels.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1477–1491},
numpages = {15}
}

@article{10.1109/TASLP.2017.2696307,
author = {Williamson, Donald S. and Wang, DeLiang and Williamson, Donald S. and DeLiang Wang},
title = {Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2696307},
doi = {10.1109/TASLP.2017.2696307},
abstract = {In real-world situations, speech is masked by both background noise and reverberation, which negatively affect perceptual quality and intelligibility. In this paper, we address monaural speech separation in reverberant and noisy environments. We perform dereverberation and denoising using supervised learning with a deep neural network. Specifically, we enhance the magnitude and phase by performing separation with an estimate of the complex ideal ratio mask. We define the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech. Our approach is evaluated using simulated and real room impulse responses, and with background noises. The proposed approach improves objective speech quality and intelligibility significantly. Evaluations and comparisons show that it outperforms related methods in many reverberant and noisy environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1492–1501},
numpages = {10}
}

@article{10.1109/TASLP.2017.2700940,
author = {Vairetti, Giacomo and De Sena, Enzo and Catrysse, Michael and Jensen, Soren Holdt and Moonen, Marc and van Waterschoot, Toon and Vairetti, Giacomo and De Sena, Enzo and Catrysse, Michael and Jensen, Soren Holdt and Moonen, Marc and van Waterschoot, Toon},
title = {A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2700940},
doi = {10.1109/TASLP.2017.2700940},
abstract = {Parametric modeling of room acoustics aims at representing room transfer functions by means of digital filters and finds application in many acoustic signal enhancement algorithms. In previous work by other authors, the use of orthonormal basis functions OBFs for modeling room acoustics has been proposed. Some advantages of OBF models over all-zero and pole-zero models have been illustrated, mainly focusing on the fact that OBF models typically require less model parameters to provide the same model accuracy. In this paper, it is shown that the orthogonality of the OBF model brings several additional advantages, which can be exploited if a suitable algorithm for identifying the OBF model parameters is applied. Specifically, the orthogonality of OBF models does not only lead to improved model efficiency as pointed out in previous work, but also leads to improved model scalability and model stability. Its appealing scalability property derives from a previously unexplored interpretation of the OBF model as an approximation to a solution of the inhomogeneous acoustic wave equation. Following this interpretation, a novel identification algorithm is proposed that takes advantage of the OBF model orthogonality to deliver efficient, scalable, and stable OBF model estimates, which is not necessarily the case for nonlinear estimation techniques that are normally applied.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1547–1561},
numpages = {15}
}

@article{10.1109/TASLP.2017.2699325,
author = {Kim, Jung-Hee and Kim, Jin and Jeon, Jae Hyeon and Nam, Sang Won and Jung-Hee Kim and Jin Kim and Jae Hyeon Jeon and Sang Won Nam},
title = {Delayless Individual-Weighting-Factors Sign Subband Adaptive Filter With Band-Dependent Variable Step-Sizes},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2699325},
doi = {10.1109/TASLP.2017.2699325},
abstract = {Sign subband adaptive filter algorithms with individual weighting factors IWF-SSAF were recently proposed to achieve improved convergence performance in impulsive noise environments by fully utilizing the decorrelating property of subband adaptive filters. However, such sign subband adaptive filter SSAF algorithms may have an inherent signal path delay problem for real-time applications, and thus, can be restrictively applied. In this paper, a delayless IWF-SSAF with band-dependent variable step-sizes BD-VSS, robust in impulsive noise environments, is proposed for real-time applications, whereby two delayless filter structures developed for the ℓ2 -norm-based SAF are employed along with the IWF-SSAF. In particular, a BD-VSS algorithm is also introduced for better convergence by applying the recent ℓ1-norm minimization technique to respective subband. Finally, the performance of the proposed delayless IWF-SSAF with BD-VSS is verified in various impulsive interference environments such as system identification with impulsive noise, acoustic echo cancellation in a double-talk scenario, and active impulsive noise control.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1526–1534},
numpages = {9}
}

@article{10.1109/TASLP.2017.2694704,
author = {Xu, Xinzhou and Deng, Jun and Cummins, Nicholas and Zhang, Zixing and Wu, Chen and Zhao, Li and Schuller, Bjorn and Xinzhou Xu and Jun Deng and Cummins, Nicholas and Zixing Zhang and Chen Wu and Li Zhao and Schuller, Bjorn},
title = {A Two-Dimensional Framework of Multiple Kernel Subspace Learning for Recognizing Emotion in Speech},
year = {2017},
issue_date = {July 2017},
publisher = {IEEE Press},
volume = {25},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2694704},
doi = {10.1109/TASLP.2017.2694704},
abstract = {As a highly active topic in computational paralinguistics, speech emotion recognition SER aims to explore ideal representations for emotional factors in speech. In order to improve the performance of SER, multiple kernel learning MKL dimensionality reduction has been utilized to obtain effective information for recognizing emotions. However, the solution of MKL usually provides only one nonnegative mapping direction for multiple kernels; this may lead to loss of valuable information. To address this issue, we propose a two-dimensional framework for multiple kernel subspace learning. This framework provides more linear combinations on the basis of MKL without nonnegative constraints, which preserves more information in the learning procedures. It also leverages both of MKL and two-dimensional subspace learning, combining them into a unified structure. To apply the framework to SER, we also propose an algorithm, namely generalised multiple kernel discriminant analysis GMKDA, by employing discriminant embedding graphs in this framework. GMKDA takes advantage of the additional mapping directions for multiple kernels in the proposed framework. In order to evaluate the performance of the proposed algorithm a wide range of experiments is carried out on several key emotional corpora. These experimental results demonstrate that the proposed methods can achieve better performance compared with some conventional and subspace learning methods in dealing with SER.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1436–1449},
numpages = {14}
}

@article{10.1109/TASLP.2017.2699334,
author = {Richard, G. and Virtanen, T. and Bello, J. P. and Ono, N. and Glotin, H.},
title = {Introduction to the Special Section on Sound Scene and Event Analysis},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2699334},
doi = {10.1109/TASLP.2017.2699334},
abstract = {The papers in this special section are devoted to the growing field of acoustic scene classification and acoustic event recognition. Machine listening systems still have difficulties to reach the ability of human listeners in the analysis of realistic acoustic scenes. If sustained research efforts have been made for decades in speech recognition, speaker identification and to a lesser extent in music information retrieval, the analysis of other types of sounds, such as environmental sounds, is the subject of growing interest from the community and is targeting an ever increasing set of audio categories. This problem appears to be particularly challenging due to the large variety of potential sound sources in the scene, which may in addition have highly different acoustic characteristics, especially in bioacoustics. Furthermore, in realistic environments, multiple sources are often present simultaneously, and in reverberant conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1169–1171},
numpages = {3}
}

@article{10.1109/TASLP.2017.2690565,
author = {Stowell, Dan and Benetos, Emmanouil and Gill, Lisa F. and Stowell, Dan and Benetos, Emmanouil and Gill, Lisa F.},
title = {On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690565},
doi = {10.1109/TASLP.2017.2690565},
abstract = {We introduce a novel approach to studying animal behavior and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behavior researchers to study individual vocalizations of freely behaving animals, even in the field. However, such devices may record more than an animal's vocal behavior, and have the potential to be used for investigating specific activities movement and context background within which vocalizations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: A scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis. We analyze recordings made with Eurasian jackdaws Corvus monedula in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalization from one individual to another. we consider the interrelation of “scenes” and “events” in this particular task, and issues of temporal resolution.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1193–1206},
numpages = {14}
}

@article{10.1109/TASLP.2016.2625423,
author = {Jameel, Abu Shafin Mohammad Mahdee and Fattah, Shaikh Anowarul and Goswami, Rajib and Zhu, Wei-Ping and Ahmad, M. Omair and Jameel, Abu Shafin Mohammad Mahdee and Fattah, Shaikh Anowarul and Goswami, Rajib and Wei-Ping Zhu and Ahmad, M. Omair},
title = {Noise Robust Formant Frequency Estimation Method Based on Spectral Model of Repeated Autocorrelation of Speech},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2625423},
doi = {10.1109/TASLP.2016.2625423},
abstract = {In this paper, a noise robust formant frequency estimation scheme is developed based on a spectral model matching algorithm. Considering the vocal tract as an autoregressive system, a spectral model of repeated autocorrelation function RACF of band-limited speech signal is proposed. It is shown that because of the repeated autocorrelation operation on band-limited signal, the proposed model can exhibit prominent formant characteristics. First from given noisy speech observations, an adaptive band selection criterion is developed. Next, on each resulting band-limited noisy speech signal, a repeated autocorrelation operation is carried out, which not only reduces the effect of noise but also strengthens the dominant poles corresponding to the formant frequencies. Finally, spectrum of the RACF is computed and instead of direct spectral peak picking, a model fitting scheme is introduced to find out model parameters which lead to formant estimation. The proposed algorithm has been tested on natural vowels as well as some naturally spoken sentences in the presence of different environmental noises. It is found that the proposed scheme provides better formant estimation accuracy in comparison to some of the existing methods at low levels of signal-to-noise ratio.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1357–1370},
numpages = {14}
}

@article{10.1109/TASLP.2017.2690561,
author = {Rakotomamonjy, Alain and Rakotomamonjy, Alain},
title = {Supervised Representation Learning for Audio Scene Classification},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690561},
doi = {10.1109/TASLP.2017.2690561},
abstract = {This paper investigates the use of supervised feature learning approaches for extracting relevant and discriminative features from acoustic scene recordings. Owing to the recent release of open datasets for acoustic scene classification problems, representation learning techniques can now be envisioned for solving the problem of feature extraction. This paper makes a step toward this goal by first introducing a supervised nonnegative matrix factorization SNMF. Our goal through this SNMF is to induce the matrix decomposition to carry out discriminative information in addition to the usual generative ones. We achieve this objective by augmenting the nonnegative matrix factorization optimization problem with a novel loss function related to class labels of each column of the matrix to decompose. While the scale of the datasets available is still small compared to those available in computer vision, we have studied models based on convolutional neural networks. We have analyzed the performances of these models on the DCASE-16 dataset and a corrected version of the LITIS Rouen one. Our experiments show that despite the small-scale setting, supervised feature learning is favorably competitive compared to the current state-of-the-art features. We also point out that for smaller scale dataset, SNMF is indeed slightly less prone to overfitting than convolutional neural networks. While the performances of these learned features are interesting per se, a deeper analysis of their behavior in the acoustic scene problem context raises open and difficult questions that we believe, need to be addressed for further performance breakthroughs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1253–1265},
numpages = {13}
}

@article{10.1109/TASLP.2017.2690575,
author = {Cakir, Emre and Parascandolo, Giambattista and Heittola, Toni and Huttunen, Heikki and Virtanen, Tuomas and Cakir, Emre and Parascandolo, Giambattista and Heittola, Toni and Huttunen, Heikki and Virtanen, Tuomas},
title = {Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690575},
doi = {10.1109/TASLP.2017.2690575},
abstract = {Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks CNNs are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks RNNs are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a convolutional recurrent neural network CRNN and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1291–1303},
numpages = {13}
}

@article{10.1109/TASLP.2017.2690573,
author = {Trowitzsch, Ivo and Mohr, Johannes and Kashef, Youssef and Obermayer, Klaus and Trowitzsch, Ivo and Mohr, Johannes and Kashef, Youssef and Obermayer, Klaus},
title = {Robust Detection of Environmental Sounds in Binaural Auditory Scenes},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690573},
doi = {10.1109/TASLP.2017.2690573},
abstract = {In realistic acoustic scenes, the detection of particular types of environmental sounds is often impeded by the simultaneous presence of multiple sound sources. In this work, we use simulations to systematically investigate the impact of superimposed distractor sources on sound type classification in a binaural robotic system and suggest techniques for increasing the robustness under such conditions. First, we demonstrate that by superimposing target sounds with strongly varying general environmental sounds during training, sound type classifiers are less affected by the presence of a distractor source. Moreover, we show that generalization performance of such models depends on how similar the angular source configuration and the signal-to-noise ratio are to the conditions under which the models were trained. Based on these results, we demonstrate how robust models can be obtained by including a variety of different conditions in the training data, a procedure called multi-conditional training. We evaluate this technique training both with ambient sources as well as with point sources with varying angular configurations, and show that this is an effective approach to produce models with close-to-optimal performance under a wide range of conditions. Moreover, we investigate the impact of head orientation and find that it has a significant influence on classification performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1344–1356},
numpages = {13}
}

@article{10.1109/TASLP.2017.2692304,
author = {Li, Na and Mak, Man-Wai and Chien, Jen-Tzung and Na Li and Man-Wai Mak and Jen-Tzung Chien},
title = {DNN-Driven Mixture of PLDA for Robust Speaker Verification},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2692304},
doi = {10.1109/TASLP.2017.2692304},
abstract = {The mismatch between enrollment and test utterances due to different types of variabilities is a great challenge in speaker verification. Based on the observation that the SNR-level variability or channel-type variability causes heterogeneous clusters in i-vector space, this paper proposes to apply supervised learning to drive or guide the learning of probabilistic linear discriminant analysis PLDA mixture models. Specifically, a deep neural network DNN is trained to produce the posterior probabilities of different SNR levels or channel types given i-vectors as input. These posteriors then replace the posterior probabilities of indicator variables in the mixture of PLDA. The discriminative training causes the mixture model to perform more reasonable soft divisions of the i-vector space as compared to the conventional mixture of PLDA. During verification, given a test i-vector and a target-speaker's i-vector, the marginal likelihood for the same-speaker hypothesis is obtained by summing the component likelihoods weighted by the component posteriors produced by the DNN, and likewise for the different-speaker hypothesis. Results based on NIST 2012 SRE demonstrate that the proposed scheme leads to better performance under more realistic situations where both training and test utterances cover a wide range of SNRs and different channel types. Unlike the previous SNR-dependent mixture of PLDA which only focuses on SNR mismatch, the proposed model is more general and is potentially applicable to addressing different types of variability in speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1371–1383},
numpages = {13}
}

@article{10.1109/TASLP.2017.2690579,
author = {Sanchez-Hevia, Hector A. and Ayllon, David and Gil-Pita, Roberto and Rosa-Zurera, Manuel and Sanchez-Hevia, Hector A. and Ayllon, David and Gil-Pita, Roberto and Rosa-Zurera, Manuel},
title = {Maximum Likelihood Decision Fusion for Weapon Classification in Wireless Acoustic Sensor Networks},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690579},
doi = {10.1109/TASLP.2017.2690579},
abstract = {Gunshot acoustic analysis is a field with many practical applications, but due to the multitude of factors involved in the generation of the acoustic signature of firearms, it is not a trivial task. The main problem arises with the strong spatial dependence shown by the recorded waveforms even when dealing with the same weapon. However, this can be lessen by using a spatially diverse receiver such as a wireless acoustic sensor network. In this work, we address multichannel acoustic weapon classification using spatial information and a novel decision fusion rule based on it. We propose a fusion rule based on maximum likelihood estimation that takes advantage of diverse classifier ensembles to improve upon classic decision fusion techniques. Classifier diversity comes from a spatial segmentation that is performed locally at each node. The same segmentation is also used to improve the accuracy of the local classification by means of a divide and conquer approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1172–1182},
numpages = {11}
}

@article{10.1109/TASLP.2017.2690574,
author = {Grzeszick, Rene and Plinge, Axel and Fink, Gernot A. and Grzeszick, Rene and Plinge, Axel and Fink, Gernot A.},
title = {Bag-of-Features Methods for Acoustic Event Detection and Classification},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690574},
doi = {10.1109/TASLP.2017.2690574},
abstract = {The detection and classification of acoustic events in various environments is an important task. Its applications range from multimedia analysis to surveillance of humans or even animal life. Several of these tasks require the capability of online processing. Besides many approaches that tackle the task of acoustic event detection, methods that are based on the well known bag-of-features principle also emerged into the field. Acoustic features are calculated for all frames in a given time window. Then, applying the bag-of-features concept, these features are quantized with respect to a learned codebook and a histogram representation is computed. Bag-of-features approaches are particularly interesting for online processing as they have a low computational cost. In this paper, the bag-of-features principle and various extensions are reviewed, including soft quantization, supervised codebook learning, and temporal modeling. Furthermore, Mel and Gammatone frequency cepstral coefficients that originate from psychoacoustic models are used as the underlying feature set for the bag-of-features. The possibility of fusing the results of multiple channels in order to improve the robustness is shown. Two databases are used for the experiments: The DCASE 2013 office live dataset and the ITC-IRST multichannel dataset.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1242–1252},
numpages = {11}
}

@article{10.1109/TASLP.2017.2690559,
author = {Imoto, Keisuke and Ono, Nobutaka and Imoto, Keisuke and Ono, Nobutaka},
title = {Spatial Cepstrum as a Spatial Feature Using a Distributed Microphone Array for Acoustic Scene Analysis},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690559},
doi = {10.1109/TASLP.2017.2690559},
abstract = {In this paper, with the aim of using the spatial information obtained from a distributed microphone array employed for acoustic scene analysis, we propose a robust and efficient method, which is called the spatial cepstrum. In our approach, similarly to the cepstrum, which is widely used as a spectral feature, the logarithm of the amplitude in multichannel observation is converted to a feature vector by a linear orthogonal transformation. This linear orthogonal transformation is achieved by principal component analysis PCA in general. Moreover, we also show that for a circularly symmetric microphone arrangement with an isotropic sound field, PCA is identical to the inverse discrete Fourier transform and the spatial cepstrum exactly corresponds to the cepstrum. The proposed approach does not require the positions of the microphones and is robust against the synchronization mismatch of channels, thus ensuring its suitability for use with a distributed microphone array. Experimental results obtained using actual environmental sounds verify the validity of our approach even when a smaller feature dimension than the original one is used, which is achieved by dimensionality reduction through PCA. Additionally, experimental results also indicate that the robustness of the proposed method is satisfactory for observations that have the synchronization mismatch of channels.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1335–1343},
numpages = {9}
}

@article{10.1109/TASLP.2017.2690562,
author = {Koluguri, Nithin Rao and Meenakshi, G. Nisha and Ghosh, Prasanta Kumar and Koluguri, Nithin Rao and Meenakshi, G. Nisha and Ghosh, Prasanta Kumar},
title = {Spectrogram Enhancement Using Multiple Window Savitzky-Golay MWSG Filter for Robust Bird Sound Detection},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690562},
doi = {10.1109/TASLP.2017.2690562},
abstract = {Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay MWSG filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time-frequency T-F plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score $10.24%$ relative compared to the best of the six baseline schemes considered in this work.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1183–1192},
numpages = {10}
}

@article{10.1109/TASLP.2017.2690558,
author = {Yang, Wenjun and Krishnan, Sridhar and Wenjun Yang and Krishnan, Sridhar},
title = {Combining Temporal Features by Local Binary Pattern for Acoustic Scene Classification},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690558},
doi = {10.1109/TASLP.2017.2690558},
abstract = {The popular frequency-domain features Mel-frequency cepstral coefficients MFCCs have been widely used for the task of acoustic scene classification ASC. The MFCC feature vector describes only the power spectral envelope of a single frame, but it seems like environmental audio signal would benefit from information in the temporal dynamics. However, the classic approach of integrating them would lose this important information. Here, we adopt local binary pattern LBP as a tool to characterize the latent information on the temporal dynamics. The frame-level MFCC features are viewed as a 2-D image, where we use LBP to encode the evolution process. Besides, some complementary spectral features such as spectral centroid SC, spectral bandwidth SBW is utilized to further improve the ASC performance. The proposed features are then fed into an ensemble classifier called D3C for recognizing environmental sounds. The results show that the proposed method was able to achieve a classification improvement of 8% compared to the baseline system. Our work presented a new method for combing the temporal features, demonstrating the significance of the temporal evolution features for characterizing the environmental sound.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1315–1321},
numpages = {7}
}

@article{10.1109/TASLP.2017.2693566,
author = {Wu, Kai and Reju, Vaninirappuputhenpurayil Gopalan and Khong, Andy W. H. and Goh, Shu Ting and Kai Wu and Reju, Vaninirappuputhenpurayil Gopalan and Khong, Andy W. H. and Shu Ting Goh},
title = {Swarm Intelligence Based Particle Filter for Alternating Talker Localization and Tracking Using Microphone Arrays},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2693566},
doi = {10.1109/TASLP.2017.2693566},
abstract = {We address the problem of localizing and tracking alternating moving or stationary talkers using microphone arrays in a room environment. One of the main challenges is the frequent and possibly abrupt change of talker positions, which requires the algorithm to capture the active talker rapidly. In addition, the presence of interference, background noise, and room reverberation degrades the tracking performance. We propose a new algorithm that jointly exploits the advantages of the particle filter PF and particle swarm intelligence. The PF is used as a general tracking framework, which incorporates a proposed alternating source-dynamic model for recursive estimation of talker position. Unlike the conventional PF, where particles operate independently in the particle sampling stage, the use of swarm intelligence allows particles to interact with each other, thereby improving convergence toward the active talker location. In addition, the memory mechanism in swarm intelligence allows particles to remain at their previous best-fit state estimate when signals are corrupted by interference, noise, and/or reverberation. Simulations and experiments were conducted to demonstrate the effectiveness of the proposed algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1384–1397},
numpages = {14}
}

@article{10.1109/TASLP.2017.2690567,
author = {Carroll, Brandon T. and Whitaker, Bradley M. and Dayley, Wayne and Anderson, David V. and Carroll, Brandon T. and Whitaker, Bradley M. and Dayley, Wayne and Anderson, David V.},
title = {Outlier Learning via Augmented Frozen Dictionaries},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690567},
doi = {10.1109/TASLP.2017.2690567},
abstract = {A frozen dictionary learning method is proposed to automatically separate the aspects of environmental audio that are different between normal data and anomalous data. The approach involves learning a dictionary-based sparse representation of the normal data, then freezing this portion of the dictionary while an added portion of the dictionary is learned on the anomalous data. Two dictionary learning algorithms are modified to allow training some elements while holding others constant. Both algorithms demonstrate the ability to separate the anomalies for two sets of test data. One set is chicken recordings with human crowd noise anomalies artificially added at -6 dB. The other set consists of recordings of chickens that are healthy and sick. Both dictionary methods are able to identify data anomalies with high accuracy and precision.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1207–1215},
numpages = {9}
}

@article{10.1109/TASLP.2017.2690568,
author = {Dov, David and Talmon, Ronen and Cohen, Israel and Dov, David and Talmon, Ronen and Cohen, Israel},
title = {Multimodal Kernel Method for Activity Detection of Sound Sources},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690568},
doi = {10.1109/TASLP.2017.2690568},
abstract = {We consider the problem of acoustic scene analysis of multiple sound sources. In our setting, the sound sources are measured by a single microphone, and a particular source of interest is also captured by a video camera during a short time interval. The goal in this paper is to detect the activity of the source of interest even when the video data are missing, while ignoring the other sound sources. To address this problem, we propose a kernel-based algorithm that incorporates the audio-visual data by a combination of affinity kernels, constructed separately from the audio and the video data. We introduce a distance measure between data points that is associated with the source of interest, while reducing the effect of the other interfering sources. Using this distance, we devise a measure for the presence of the source of interest, which is naturally extended to time intervals, in which only the audio signal is available. Experimental results demonstrate the improved performance of the proposed algorithm compared to competing approaches implying the significance of the video signal in the analysis of complex acoustic scenes.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1322–1334},
numpages = {13}
}

@article{10.1109/TASLP.2017.2690564,
author = {Phan, Huy and Hertel, Lars and Maass, Marco and Koch, Philipp and Mazur, Radoslaw and Mertins, Alfred and Huy Phan and Hertel, Lars and Maass, Marco and Koch, Philipp and Mazur, Radoslaw and Mertins, Alfred},
title = {Improved Audio Scene Classification Based on Label-Tree Embeddings and Convolutional Neural Networks},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690564},
doi = {10.1109/TASLP.2017.2690564},
abstract = {In this paper, we present an efficient approach for audio scene classification. We aim at learning representations for scene examples by exploring the structure of their class labels. A category taxonomy is automatically learned by collectively optimizing a tree-structured clustering of the given labels into multiple metaclasses. A scene recording is then transformed into a label-tree embedding image. Elements of the image represent the likelihoods that the scene instance belongs to the metaclasses. We investigate classification with label-tree embedding features learned from different low-level features as well as their fusion. We show that the combination of multiple features is essential to obtain good performance. While averaging label-tree embedding images over time yields good performance, we argue that average pooling possesses an intrinsic shortcoming. We alternatively propose an improved classification scheme to bypass this limitation. We aim at automatically learning common templates that are useful for the classification task from these images using simple but tailored convolutional neural networks. The trained networks are then employed as a feature extractor that matches the learned templates across a label-tree embedding image and produce the maximum matching scores as features for classification. Since audio scenes exhibit rich content, template learning and matching on low-level features would be inefficient. With label-tree embedding features, we have quantized and reduced the low-level features into the likelihoods of the metaclasses, on which the template learning and matching are efficient. We study both training convolutional neural networks on stacked label-tree embedding images and multistream networks. Experimental results on the DCASE2016 and LITIS Rouen datasets demonstrate the efficiency of the proposed methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1278–1290},
numpages = {13}
}

@article{10.1109/TASLP.2017.2690569,
author = {Schroder, Jens and Moritz, Niko and Anemuller, Jorn and Goetze, Stefan and Kollmeier, Birger and Schr\"{o}der, Jens and Moritz, Niko and Anem\"{u}ller, J\"{o}rn and Goetze, Stefan and Kollmeier, Birger},
title = {Classifier Architectures for Acoustic Scenes and Events: Implications for DNNs, TDNNs, and Perceptual Features from DCASE 2016},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690569},
doi = {10.1109/TASLP.2017.2690569},
abstract = {This paper evaluates neural network NN based systems and compares them to Gaussian mixture model GMM and hidden Markov model HMM approaches for acoustic scene classification SC and polyphonic acoustic event detection AED that are applied to data of the “Detection and Classification of Acoustic Scenes and Events 2016” DCASE'16 challenge, task 1 and task 3, respectively. For both tasks, the use of deep neural networks DNNs and features based on an amplitude modulation filterbank and a Gabor filterbank GFB are evaluated and compared to standard approaches. For SC, additionally a time-delay NN approach is proposed that enables analysis of long contextual information similar to recurrent NNs but with training efforts comparable to conventional DNNs. The SC system proposed for task 1 of the DCASE'16 challenge attains a recognition accuracy of 77.5%, which is 5.6% higher compared to the DCASE'16 baseline system. For the AED task, DNNs are adopted in tandem and hybrid approaches, i.e., as part of HMM-based systems. These systems are evaluated for the polyphonic data of task 3 from the DCASE'16 challenge. Several strategies to address the issue of polyphony are considered. It is shown that DNN-based systems perform less accurate than the traditional systems for this task. Best results are achieved using GFB features in combination with a multiclass GMM-HMM back end.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1304–1314},
numpages = {11}
}

@article{10.1109/TASLP.2017.2690570,
author = {Bisot, Victor and Serizel, Romain and Essid, Slim and Richard, Gael and Bisot, Victor and Serizel, Romain and Essid, Slim and Richard, Gael},
title = {Feature Learning With Matrix Factorization Applied to Acoustic Scene Classification},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690570},
doi = {10.1109/TASLP.2017.2690570},
abstract = {In this paper, we study the usefulness of various matrix factorization methods for learning features to be used for the specific acoustic scene classification ASC problem. A common way of addressing ASC has been to engineer features capable of capturing the specificities of acoustic environments. Instead, we show that better representations of the scenes can be automatically learned from time-frequency representations using matrix factorization techniques. We mainly focus on extensions including sparse, kernel-based, convolutive and a novel supervised dictionary learning variant of principal component analysis and nonnegative matrix factorization. An experimental evaluation is performed on two of the largest ASC datasets available in order to compare and discuss the usefulness of these methods for the task. We show that the unsupervised learning methods provide better representations of acoustic scenes than the best conventional hand-crafted features on both datasets. Furthermore, the introduction of a novel nonnegative supervised matrix factorization model and deep neural networks trained on spectrograms, allow us to reach further improvements.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1216–1229},
numpages = {14}
}

@article{10.1109/TASLP.2017.2690576,
author = {Benetos, Emmanouil and Lafay, Gregoire and Lagrange, Mathieu and Plumbley, Mark D. and Benetos, Emmanouil and Lafay, Gregoire and Lagrange, Mathieu and Plumbley, Mark D.},
title = {Polyphonic Sound Event Tracking Using Linear Dynamical Systems},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690576},
doi = {10.1109/TASLP.2017.2690576},
abstract = {In this paper, a system for polyphonic sound event detection and tracking is proposed, based on spectrogram factorization techniques and state space models. The system extends probabilistic latent component analysis PLCA and is modeled around a four-dimensional spectral template dictionary of frequency, sound event class, exemplar index, and sound state. In order to jointly track multiple overlapping sound events over time, the integration of linear dynamical systems LDS within the PLCA inference is proposed. The system assumes that the PLCA sound event activation is the noisy observation in an LDS, with the latent states corresponding to the true event activations. LDS training is achieved using fully observed data, making use of ground truth-informed event activations produced by the PLCA-based model. Several LDS variants are evaluated, using polyphonic datasets of office sounds generated from an acoustic scene simulator, as well as real and synthesized monophonic datasets for comparative purposes. Results show that the integration of LDS tracking within PLCA leads to an improvement of +8.5-10.5% in terms of frame-based F-measure as compared to the use of the PLCA model alone. In addition, the proposed system outperforms several state-of-the-art methods for the task of polyphonic sound event detection.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1266–1277},
numpages = {12}
}

@article{10.1109/TASLP.2017.2690563,
author = {Xu, Yong and Huang, Qiang and Wang, Wenwu and Foster, Peter and Sigtia, Siddharth and Jackson, Philip J. B. and Plumbley, Mark D. and Yong Xu and Qiang Huang and Wenwu Wang and Foster, Peter and Sigtia, Siddharth and Jackson, Philip J. B. and Plumbley, Mark D.},
title = {Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging},
year = {2017},
issue_date = {June 2017},
publisher = {IEEE Press},
volume = {25},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2690563},
doi = {10.1109/TASLP.2017.2690563},
abstract = {Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper, we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a shrinking deep neural network DNN framework incorporating unsupervised feature learning to handle the multilabel classification task. For the acoustic modeling, a large set of contextual frames of the chunk are fed into the DNN to perform a multilabel classification for the expected tags, considering that only chunk or utterance level rather than frame-level labels are available. Dropout and background noise aware training are also adopted to improve the generalization capability of the DNNs. For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep denoising auto-encoder syDAE or asyDAE to generate new data-driven features from the logarithmic Mel-filter banks features. The new features, which are smoothed against background noise and more compact with contextual information, can further improve the performance of the DNN baseline. Compared with the standard Gaussian mixture model baseline of the DCASE 2016 audio tagging challenge, our proposed method obtains a significant equal error rate EER reduction from 0.21 to 0.13 on the development set. The proposed asyDAE system can get a relative 6.7% EER reduction compared with the strong DNN baseline on the development set. Finally, the results also show that our approach obtains the state-of-the-art performance with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while EER of the first prize of this challenge is 0.17.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1230–1241},
numpages = {12}
}

@article{10.1109/TASLP.2017.2678166,
author = {Prusa, Zdenek and Balazs, Peter and Sondergaard, Peter Lempel and Prusa, Zdenek and Balazs, Peter and Sondergaard, Peter Lempel},
title = {A Noniterative Method for Reconstruction of Phase From STFT Magnitude},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2678166},
doi = {10.1109/TASLP.2017.2678166},
abstract = {A noniterative method for the reconstruction of the short-time fourier transform STFT phase from the magnitude is presented. The method is based on the direct relationship between the partial derivatives of the phase and the logarithm of the magnitude of the un-sampled STFT with respect to the Gaussian window. Although the theory holds in the continuous setting only, the experiments show that the algorithm performs well even in the discretized setting discrete Gabor transform with low redundancy using the sampled Gaussian window, the truncated Gaussian window and even other compactly supported windows such as the Hann window. Due to the noniterative nature, the algorithm is very fast and it is suitable for long audio signals. Moreover, solutions of iterative phase reconstruction algorithms can be improved considerably by initializing them with the phase estimate provided by the present algorithm. We present an extensive comparison with the state-of-the-art algorithms in a reproducible manner.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1154–1164},
numpages = {11}
}

@article{10.1109/TASLP.2017.2678162,
author = {Kanda, Naoyuki and Lu, Xugang and Kawai, Hisashi and Kanda, Naoyuki and Xugang Lu and Kawai, Hisashi},
title = {Maximum-a-Posteriori-Based Decoding for End-to-End Acoustic Models},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2678162},
doi = {10.1109/TASLP.2017.2678162},
abstract = {This paper presents a novel decoding framework for acoustic models AMs based on end-to-end neural networks e.g., connectionist temporal classification. The end-to-end training of AMs has recently demonstrated high accuracy and efficiency in automatic speech recognition ASR. When using the trained AM in decoding, although a language model LM is implicitly involved in such an end-to-end AM, it is still essential to integrate an external LM trained with a large text corpus to achieve the best results. While there is no theoretical justification, most of the studies suggest using a naive interpolation of the end-to-end AM score and the external LM score, empirically. In this paper, we propose a more theoretically sound decoding framework derived from a maximization of the posterior probability of a word sequence given an observation. As a consequence of the theory, the subword LM is newly introduced to seamlessly integrate the external LM score with the end-to-end AM score. Our proposed method can be achieved by a small modification of the conventional weighted finite-state transducer-based implementation, without having to heavily increase the graph size. We tested the proposed decoding framework on ASR experiments with the Corpus of the Wall Street Journal and the Corpus of Spontaneous Japanese. The results showed that the proposed framework achieved significant and consistent improvements over the conventional interpolation-based decoding framework.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1023–1034},
numpages = {12}
}

@article{10.1109/TASLP.2017.2676356,
author = {Li, Songbin and Jia, Yizhen and Kuo, C. -C. Jay and Songbin Li and Yizhen Jia and Kuo, C.-C Jay},
title = {Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2676356},
doi = {10.1109/TASLP.2017.2676356},
abstract = {Steganalysis of the quantization index modulation QIM steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization VQ codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network QCCN based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine SVM classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1011–1022},
numpages = {12}
}

@article{10.1109/TASLP.2017.2671422,
author = {Yang, Feiran and Enzner, Gerald and Yang, Jun and Yang, Feiran and Enzner, Gerald and Jun Yang},
title = {Statistical Convergence Analysis for Optimal Control of DFT-Domain Adaptive Echo Canceler},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2671422},
doi = {10.1109/TASLP.2017.2671422},
abstract = {The frequency-domain adaptive filter FDAF is widely used in echo cancellation systems due to its low complexity and fast convergence rate. However, the FDAF algorithm with a fixed step size exhibits a tradeoff among the convergence rate, steady-state misalignment, tracking ability, and robustness to near-end speech interferences. Several variable step-size FDAF algorithms were presented to address this problem. However, the state-of-the-art variable step-size FDAF algorithms did not handle this problem comprehensively. This paper presents a new robust variable step-size control approach to the FDAF algorithm. Based on a statistical analysis of the FDAF algorithm, an optimal step size for each frequency bin is derived by minimizing the mean-square deviation MSD between the true weight vector and estimated weight vector at each frame. Calculation of the step size requires the system distance and the observation noise power spectral density PSD. The system distance is estimated using the deterministic recursive equations of MSD and the noise PSD is computed using the magnitude squared coherence function between the far-end signal and error signal. Moreover, a close link between the proposed FDAF and the frequency-domain Kalman filter is revealed. Specifically, the work presented here can be understood as a means to adaptively monitor and control the underlying acoustic state space of the Kalman filter, including means for fast readaptation of the adaptive filter after abrupt echo path changes. Simulation results demonstrate that the proposed algorithm can achieve fast convergence and low steady-state misalignment. Furthermore, the algorithm is robust to the double-talk interferences, but it does not require an explicit double-talk detector.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1095–1106},
numpages = {12}
}

@article{10.1109/TASLP.2017.2655258,
author = {Schwartz, Ofer and Gannot, Sharon and Habets, Emanuel A. P. and Schwartz, Ofer and Gannot, Sharon and Habets, Emanuel A. P.},
title = {Multispeaker LCMV Beamformer and Postfilter for Source Separation and Noise Reduction},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2655258},
doi = {10.1109/TASLP.2017.2655258},
abstract = {The problem of source separation and noise reduction using multiple microphones is addressed. The minimum mean square error MMSE estimator for the multispeaker case is derived and a novel decomposition of this estimator is presented. The MMSE estimator is decomposed into two stages: first, a multispeaker linearly constrained minimum variance LCMV beamformer BF; and second, a subsequent multispeaker Wiener postfilter. The first stage separates and enhances the signals of the individual speakers by utilizing the spatial characteristics of the speakers [as manifested by the respective acoustic transfer functions ATFs] and the noise power spectral density PSD matrix, while the second stage exploits the speakers' PSD matrix to reduce the residual noise at the output of the first stage. The output vector of the multispeaker LCMV BF is proven to be the sufficient statistic for estimating the marginal speech signals in both the classic sense and the Bayesian sense. The log spectral amplitude estimator for the multispeaker case is also derived given the multispeaker LCMV BF outputs. The performance evaluation was conducted using measured ATFs and directional noise with various signal-to-noise ratio levels. It is empirically verified that the multispeaker postfilters are beneficial in terms of signal-to-interference plus noise ratio improvement when compared with the single-speaker postfilter.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {940–951},
numpages = {12}
}

@article{10.1109/TASLP.2016.2620718,
author = {Airaksinen, Manu and Backstrom, Tom and Alku, Paavo and Airaksinen, Manu and Backstrom, Tom and Alku, Paavo},
title = {Quadratic Programming Approach to Glottal Inverse Filtering by Joint Norm-1 and Norm-2 Optimization},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2620718},
doi = {10.1109/TASLP.2016.2620718},
abstract = {This study proposes an approach for glottal inverse filtering of acoustic speech signals using quadratic programming QPR. The method aims to jointly model the effect of vocal tract and lip radiation with a single filter whose coefficients are optimized using QPR. This optimization is based on the principles of closed phase analysis, where the contribution of the glottal source is attenuated in optimizing the inverse model of the vocal tract. By expressing the optimization problem in terms of the output of a filter, we can apply physically motivated optimization such as flatness of the closed phase. The proposed method was objectively evaluated using a synthetic Liljencrants-Fant model based test set of sustained vowels, as well as a real speech test set where the glottal flow estimates' closed phases were compared in terms of their flatness. The results based on synthetic speech indicate that the proposed method is robust to changes in f0, and state-of-the-art quality results were obtained for high-pitched voices, when f0 is in the range 330-450 Hz. The results based on real speech indicate that the proposed method produces glottal flow estimates that have flatter closed phases with less formant ripple in comparison to estimates computed with known reference methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {929–939},
numpages = {11}
}

@article{10.1109/TASLP.2017.2689681,
author = {Huang, Gongping and Benesty, Jacob and Chen, Jingdong and Gongping Huang and Benesty, Jacob and Jingdong Chen},
title = {On the Design of Frequency-Invariant Beampatterns With Uniform Circular Microphone Arrays},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2689681},
doi = {10.1109/TASLP.2017.2689681},
abstract = {This paper deals with two critical issues about uniform circular arrays UCAs: frequency-invariant response and steering flexibility. It focuses on some optimal design of frequency-invariant beampatterns in any desired direction along the sensor plane. The major contributions are as follows. 1 We explain how to include the steering information in the desired directivity pattern. 2 We show that the optimal approximation of the beamformer's beampattern with a UCA from a least-squares error perspective is the Jacobi-Anger expansion. 3 We develop an approach to the design of any desired symmetric directivity pattern, where the deduced beampattern is almost frequency invariant and its main beam can be pointed to any wanted direction in the sensor plane. 4 With the proposed approach, we derive an explicit form of the white noise gain WNG and the directivity factor DF, and explain clearly the white noise amplification problem at low frequencies and the DF degradation at high frequencies. The analysis also indicates that increasing the number of microphones can always improve the WNG. We show that the proposed method is a generalization of circular differential microphone arrays. The relationship between the proposed method and the so-called circular harmonics beamformers is also discussed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1140–1153},
numpages = {14}
}

@article{10.1109/TASLP.2017.2678684,
author = {Shokouhi, Navid and Hansen, John H. L. and Shokouhi, Navid and Hansen, John H. L.},
title = {Teager–Kaiser Energy Operators for Overlapped Speech Detection},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2678684},
doi = {10.1109/TASLP.2017.2678684},
abstract = {Overlapped speech is referred to a monophonic audio signal in which at least two speakers are present at the same time. In this study, the focus is on distinguishing overlapped from single-speaker speech, i.e., overlapped speech detection. We develop an overlap detection algorithm using an enhanced time-frequency representation, called Pyknogram, estimated directly from the input audio signal. Pyknograms use the Teager-Kaiser energy operator to detect resonant time-frequency units and thereby suppress nonharmonic structures. We show how the resulting Pyknograms provide high separability in terms of detecting the presence of interfering speech. Our proposed unsupervised Pyknogram-based detection results in over 30% relative improvement in overlap detection error rates across different signal-to-interference ratios SIR compared to baseline systems. In addition, a case study is presented where we evaluate speaker verification performance under different overlap conditions using the GRID database and observe that speaker verification equal error rates EER vary from 2% to 30%, depending on the average SIR values introduced to train and test sets. In order to estimate the reliability of speaker verification scores across different trials, overlap detection results are interpreted as low-level information and stacked alongside verification outputs. The resulting high-dimensional space is passed through a support vector machine classifier to find the separating hyperplane between target and imposter scores. Combining overlap detection scores with speaker verification on average yields 20% relative decrease in EER. We also provide an upper bound for this approach using existing overlap labels, which yields 23% relative improvement.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1035–1047},
numpages = {13}
}

@article{10.1109/TASLP.2017.2681742,
author = {Park, Jeongsoo and Shin, Jaeyoung and Lee, Kyogu and Park, Jeongsoo and Jaeyoung Shin and Kyogu Lee},
title = {Exploiting Continuity/Discontinuity of Basis Vectors in Spectrogram Decomposition for Harmonic-Percussive Sound Separation},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2681742},
doi = {10.1109/TASLP.2017.2681742},
abstract = {In this paper, we present a novel method for harmonic-percussive sound separation HPSS by exploiting continuity/discontinuity properties in a matrix decomposition framework. It is widely accepted in the HPSS research that the harmonic and percussive components have anisotropic characteristics: The spectrum of the harmonic components and the time activation of the percussive components are sparse, whereas the spectrum of the percussive components and the time activation of the harmonic components are smooth. However, conventional methods fail to fully utilize the characteristics leading to suboptimal performance. Based on the observations that not the degree of sparseness but the degree of fluctuation is an accurate measure for distinguishing the harmonic and percussive components, we propose a novel HPSS algorithm by incorporating the continuity control in the iterative update formula of the matrix decomposition algorithm. In doing so, we first utilize probabilistic latent component analysis with Dirichlet prior, and later reformulate the algorithm in the nonnegative matrix factorization framework to reduce the computational cost. The comparative evaluation results show that the proposed method outperforms conventional methods in terms of both objective and subjective evaluation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1061–1074},
numpages = {14}
}

@article{10.1109/TASLP.2017.2672401,
author = {Sainath, Tara N. and Weiss, Ron J. and Wilson, Kevin W. and Li, Bo and Narayanan, Arun and Variani, Ehsan and Bacchiani, Michiel and Shafran, Izhak and Senior, Andrew and Chin, Kean and Misra, Ananya and Kim, Chanwoo and Sainath, Tara N. and Weiss, Ron J. and Wilson, Kevin W. and Bo Li and Narayanan, Arun and Variani, Ehsan and Bacchiani, Michiel and Shafran, Izhak and Senior, Andrew and Chin, Kean and Misra, Ananya and Chanwoo Kim},
title = {Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2672401},
doi = {10.1109/TASLP.2017.2672401},
abstract = {Multichannel automatic speech recognition ASR systems commonly separate speech enhancement, including localization, beamforming, and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from different directions, we explore modeling the raw time-domain waveform directly. We introduce a neural network architecture, which performs multichannel filtering in the first layer of the network, and show that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target speaker direction. Next, we show how performance can be improved by factoring the first layer to separate the multichannel spatial filtering operation from a single channel filterbank which computes a frequency decomposition. We also introduce an adaptive variant, which updates the spatial filter coefficients at each time frame based on the previous inputs. Finally, we demonstrate that these approaches can be implemented more efficiently in the frequency domain. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel waveform model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {965–979},
numpages = {15}
}

@article{10.1109/TASLP.2017.2689245,
author = {Firtha, Gergely and Fiala, Peter and Schultz, Frank and Spors, Sascha and Firtha, Gergely and Fiala, Peter and Schultz, Frank and Spors, Sascha},
title = {Improved Referencing Schemes for 2.5D Wave Field Synthesis Driving Functions},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2689245},
doi = {10.1109/TASLP.2017.2689245},
abstract = {Wave Field Synthesis allows the reconstruction of an arbitrary target sound field within a listening area by using a secondary source contour of spherical monopoles. While phase correct synthesis is ensured over the whole listening area, amplitude deviations are present besides a predefined reference curve. So far, the existence and potential shapes of this reference curve was not extensively discussed in the Wave Field Synthesis literature. This paper introduces improved driving functions for 2.5D Wave Field Synthesis. The novel driving functions allow for the control of the locations of amplitude correct synthesis for arbitrarily shaped-possibly curved-secondary source distributions. This is achieved by deriving an expressive physical interpretation of the stationary phase approximation leading to the presented unified Wave Field Synthesis framework. The improved solutions are better suited for practical applications. Additionally, a consistent classification of existing implicit and explicit 2.5D sound field synthesis solutions as special cases of the unified framework is given.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1117–1127},
numpages = {11}
}

@article{10.1109/TASLP.2017.2687104,
author = {Zhang, Xueliang and Wang, DeLiang and Xueliang Zhang and DeLiang Wang},
title = {Deep Learning Based Binaural Speech Separation in Reverberant Environments},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2687104},
doi = {10.1109/TASLP.2017.2687104},
abstract = {Speech signal is usually degraded by room reverberation and additive noises in real environments. This paper focuses on separating target speech signal in reverberant conditions from binaural inputs. Binaural separation is formulated as a supervised learning problem, and we employ deep learning to map from both spatial and spectral features to a training target. With binaural inputs, we first apply a fixed beamformer and then extract several spectral features. A new spatial feature is proposed and extracted to complement the spectral features. The training target is the recently suggested ideal ratio mask. Systematic evaluations and comparisons show that the proposed system achieves very good separation performance and substantially outperforms related algorithms under challenging multisource and reverberant environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1075–1084},
numpages = {10}
}

@article{10.1109/TASLP.2017.2689241,
author = {Maestre, Esteban and Scavone, Gary P. and Smith, Julius O. and Maestre, Esteban and Scavone, Gary P. and Smith, Julius O.},
title = {Joint Modeling of Bridge Admittance and Body Radiativity for Efficient Synthesis of String Instrument Sound by Digital Waveguides},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2689241},
doi = {10.1109/TASLP.2017.2689241},
abstract = {In the context of efficient sound synthesis by digital waveguides, we present a novel methodology for joint modeling of string instrument body radiativity and driving-point bridge admittance functions, as obtained from experimental data. From our modeling framework, aimed at simulation of guitar and bowed string sound, here we focus on the body of the instrument and leave aside the strings. First, a modal decomposition of the measured bridge admittance is obtained by means of a novel frequency-domain algorithm for optimization of recursive digital filters in parallel form. Then, from extracted modal parameters, the radiativity, and admittance functions are modeled by projecting measurements over a common modal basis, enforcing passivity of the two-dimensional admittance model by means of semidefinite programming. We propose a formulation that enables the joint realization of bridge reflectance and sound radiativity as a lumped delay line termination in which a single bank of resonant filters is shared among all string reflection and body radiation outputs. Our approach provides efficient means to model two-dimensional 2-D bridge reflectance, 2-D string-string coupling, sound radiation with an arbitrary number of outputs, and implicitly vibrational energy loss from the bridge transmittance to nonradiating modes and dissipation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1128–1139},
numpages = {12}
}

@article{10.1109/TASLP.2017.2687829,
author = {Delfarah, Masood and Wang, DeLiang and Delfarah, Masood and DeLiang Wang},
title = {Features for Masking-Based Monaural Speech Separation in Reverberant Conditions},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2687829},
doi = {10.1109/TASLP.2017.2687829},
abstract = {Monaural speech separation is a fundamental problem in speech and signal processing. This problem can be approached from a supervised learning perspective by predicting an ideal time-frequency mask from features of noisy speech. In reverberant conditions at low signal-to-noise ratios SNRs, accurate mask prediction is challenging and can benefit from effective features. In this paper, we investigate an extensive set of acoustic-phonetic features extracted in adverse conditions. Deep neural networks are used as the learning machine, and separation performance is evaluated using standard objective speech intelligibility metrics. Separation performance is systematically evaluated in both nonspeech and speech interference, in a variety of SNRs, reverberation times, and direct-to-reverberant energy ratios. Considerable performance improvement is observed by using contextual information, likely due to temporal effects of room reverberation. In addition, we construct feature combination sets using a sequential floating forward selection algorithm, and combined features outperform individual ones. We also find that optimal feature sets in anechoic conditions are different from those in reverberant conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1085–1094},
numpages = {10}
}

@article{10.1109/TASLP.2017.2679603,
author = {Huang, Yi-Chin and Wu, Chung-Hsien and Chen, Yan-You and Shie, Ming-Ge and Wang, Jhing-Fa and Yi-Chin Huang and Chung-Hsien Wu and Yan-You Chen and Ming-Ge Shie and Jhing-Fa Wang},
title = {Personalized Spontaneous Speech Synthesis Using a Small-Sized Unsegmented Semispontaneous Speech},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2679603},
doi = {10.1109/TASLP.2017.2679603},
abstract = {A systematic approach is proposed to synthesizing personalized spontaneous speech using a small-sized unsegmented speech corpus of the target speaker. First, an automatic segmentation algorithm is employed to segment and label a collected semispontaneous speech corpus of the target speaker. Then, a pretrained average voice model is adapted to the voice model of the target speaker by using the segmented data. A postfilter based on modulation spectrum is adopted to further improve the speaker similarity of the synthesized speech as well as alleviate the over-smoothing problem of the synthesized speech. For generating spontaneous speech, a smoothing method applied at the prosodic word level is proposed to improve speech fluency. For objective evaluation on spontaneous speech segmentation, the segmentation accuracy of the proposed method is superior to that of Viterbi-based forced alignment. The results of subjective listening test also show that the proposed method can improve the spontaneity and speaker similarity of the synthesized speech compared to the maximum likelihood linear regression based speaker adaptation method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1048–1060},
numpages = {13}
}

@article{10.1109/TASLP.2017.2667879,
author = {Wang, Dongmei and Yu, Chengzhu and Hansen, John H. L. and Dongmei Wang and Chengzhu Yu and Hansen, John H. L.},
title = {Robust Harmonic Features for Classification-Based Pitch Estimation},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2667879},
doi = {10.1109/TASLP.2017.2667879},
abstract = {Pitch estimation in diverse naturalistic audio streams remains a challenge for speech processing and spoken language technology. In this study, we investigate the use of robust harmonic features for classification-based pitch estimation. The proposed pitch estimation algorithm is composed of two stages: pitch candidate generation and target pitch selection. Based on energy intensity and spectral envelope shape, five types of robust harmonic features are proposed to reflect pitch associated harmonic structure. A neural network is adopted for modeling the relationship between input harmonic features and output pitch salience for each specific pitch candidate. In the test stage, each pitch candidate is assessed with an output salience that indicates the potential as a true pitch value, based on its input feature vector processed through the neural network. Finally, according to the temporal continuity of pitch values, pitch contour tracking is performed using a hidden Markov model HMM, and the Viterbi algorithm is used for HMM decoding. Experimental results show that the proposed algorithm outperforms several state-of-the-art pitch estimation methods in terms of accuracy in both high and low levels of additive noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {952–964},
numpages = {13}
}

@article{10.1109/TASLP.2017.2674975,
author = {Franck, Andreas and Wang, Wenwu and Fazi, Filippo Maria and Franck, Andreas and Wenwu Wang and Fazi, Filippo Maria},
title = {Sparse $\ell _{1}$-Optimal Multiloudspeaker Panning and Its Relation to Vector Base Amplitude Panning},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2674975},
doi = {10.1109/TASLP.2017.2674975},
abstract = {Panning techniques, such as vector base amplitude panning VBAP, are a widely used practical approach for spatial sound reproduction using multiple loudspeakers. Although limited to a relatively small listening area, they are very efficient and offer good localization accuracy, timbral quality, as well as a graceful degradation of quality outside the sweet spot. The aim of this paper is to investigate optimal sound reproduction techniques that adopt some of the advantageous properties of VBAP, such as the sparsity and the locality of the active loudspeakers for the reproduction of a single audio object. To this end, we state the task of multiloudspeaker panning as an ℓ1 optimization problem. We demonstrate and prove that the resulting solutions are exactly sparse. Moreover, we show the effect of adding a nonnegativity constraint on the loudspeaker gains in order to preserve the locality of the panning solution. Adding this constraint, ℓ1-optimal panning can be formulated as a linear program. Using this representation, we prove that unique ℓ1-optimal panning solutions incorporating a nonnegativity constraint are identical to VBAP using a Delaunay triangulation for the loudspeaker setup. Using results from linear programming and duality theory, we describe properties and special cases, such as solution ambiguity, of the VBAP solution.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {996–1010},
numpages = {15}
}

@article{10.1109/TASLP.2017.2688585,
author = {Nose, Takashi and Arao, Yusuke and Kobayashi, Takao and Sugiura, Komei and Shiga, Yoshinori and Nose, Takashi and Arao, Yusuke and Kobayashi, Takao and Sugiura, Komei and Shiga, Yoshinori},
title = {Sentence Selection Based on Extended Entropy Using Phonetic and Prosodic Contexts for Statistical Parametric Speech Synthesis},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2688585},
doi = {10.1109/TASLP.2017.2688585},
abstract = {This paper proposes a sentence selection technique for constructing phonetically and prosodically balanced compact recording scripts for speech synthesis. In the conventional corpus design of speech synthesis, a greedy algorithm that maximizes phonetic coverage is often used. However, for statistical parametric speech synthesis, balances of multiple phonetic and prosodic contextual factors are important as well as the coverage. To take account of both of the phonetic and prosodic contextual balances in sentence selection, we introduce an extended entropy of phonetic and prosodic contexts, such as biphone/triphone, accent/stress/tone, and sentence length. For detailed investigation, conventional and proposed techniques are evaluated using Japanese, English, and Chinese corpora. The objective experimental results show that the proposed technique achieves better coverage and balance of contexts. In addition, speech synthesis experiments based on hidden Markov models reveal that the generated speech parameters become closer to those of the natural speech compared with other conventional sentence selection techniques. Subjective evaluations show that the proposed sentence selection based on the extended entropy improves the naturalness of the synthetic speech while maintaining the similarity to the original sample.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1107–1116},
numpages = {10}
}

@article{10.1109/TASLP.2017.2674971,
author = {Khalilian, Hanieh and Bajic, Ivan V. and Vaughan, Rodney G. and Khalilian, Hanieh and Bajic, Ivan V. and Vaughan, Rodney G.},
title = {A Simulation Study of a Three-Dimensional Sound Field Reproduction System for Immersive Communication},
year = {2017},
issue_date = {May 2017},
publisher = {IEEE Press},
volume = {25},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2674971},
doi = {10.1109/TASLP.2017.2674971},
abstract = {Immersive communication systems promise a greatly improved user experience through the use of advanced technologies tailored to various human senses, such as sight, hearing, and touch. This paper focuses on a simulation study of three-dimensional 3-D sound field reproduction SFR for immersive communication. At the transmitting end, the incident sound field is captured via a microphone array, active talkers are detected, and a clean version of the signals corresponding to active talkers are found by existing methods. The captured information is transmitted to the receiving end, where a 3-D sound field from virtual sources corresponding to active talkers is synthesized around listeners' heads by the proposed SFR method. In our system, the radiation patterns of the higher order directive loudspeakers are optimized by the constrained matching pursuit algorithm. Implementation of the directive loudspeaker patterns is not addressed here, and their deployment is assumed. Simulation results quantify the benefit of higher order loudspeakers for speech sound field synthesis in reverberant rooms.},
journal = {IEEE/ACM Trans

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr