text:
proposed methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2194–2203},
numpages = {10}
}

@article{10.1109/TASLP.2018.2858923,
author = {Kim, Younggwan and Kim, Myungjong and Goo, Jahyun and Kim, Hoirin},
title = {Learning Self-Informed Feature Contribution for Deep Learning-Based Acoustic Modeling},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2858923},
doi = {10.1109/TASLP.2018.2858923},
abstract = {In this paper, we introduce a new feature engineering approach for deep learning-based acoustic modeling, which utilizes input feature contributions. For this purpose, we propose an auxiliary deep neural network DNN called a feature contribution network FCN whose output layer is composed of sigmoid-based contribution gates. In our framework, the FCN tries to learn element-level discriminative contributions of input features and an acoustic model network AMN is trained by gated features generated by element-wise multiplication between contribution gate outputs and input features. In addition, we also propose a regularization method for the FCN, which helps the FCN to activate the minimum number of the gates. The proposed methods were evaluated on the TED-LIUM release 1 corpus. We applied the proposed methods to DNN- and long short-term memory-based AMNs. Experimental results results showed that AMNs with the FCNs consistently improved recognition performance compared with AMN-only frameworks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2204–2214},
numpages = {11}
}

@article{10.1109/TASLP.2018.2852479,
author = {Bu, Bing and Bao, Chang-chun and Jia, Mao-shen},
title = {Design of a Planar First-Order Loudspeaker Array for Global Active Noise Control},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2852479},
doi = {10.1109/TASLP.2018.2852479},
abstract = {This paper proposes a method to design a planar first-order loudspeaker array structure for global active noise control. Compared with the traditional spherical loudspeaker array, the planar array provides a practical design with flexible source locations. The planar array is capable of achieving global noise control, provided that the loudspeakers have general variable first-order responses in elevation. On $boldsymbol {x{-}y}$ plane, we use spherical harmonics to analyze the required first-order loudspeakers consisting of monopole and tangential dipole components. By exploiting the properties of the associated Legendre functions and its derivative, we can divide the primary soundfield into even harmonics controlled by the monopole component, and odd harmonics controlled by the dipole component. Through the appropriate choice of radii of circles, we avoid the ill-conditioning problem of matrix inversion and derive a robust solution for loudspeaker weights to suppress the primary noise field. Besides, we use the closely-located monopole pairs, instead of the ideal general first-order loudspeakers, to design an alternative planar array for practical implementation. As an illustration, we use several simulation examples to validate the performance of the two proposed planar loudspeaker arrays.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2240–2250},
numpages = {11}
}

@article{10.1109/TASLP.2018.2852492,
author = {Jang, Youngsoo and Ham, Jiyeon and Lee, Byung-Jun and Kim, Kee-Eung},
title = {Cross-Language Neural Dialog State Tracker for Large Ontologies Using Hierarchical Attention},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2852492},
doi = {10.1109/TASLP.2018.2852492},
abstract = {Dialog state tracking, which refers to identifying the user intent from utterances, is one of the most important tasks in dialog management. In this paper, we present our dialog state tracker developed for the fifth dialog state tracking challenge, which focused on cross-language adaptation using a very scarce machine-translated training data when compared to the size of the ontology. Our dialog state tracker is based on the bi-directional long short-term memory network with a hierarchical attention mechanism in order to spot important words in user utterances. The user intent is predicted by finding the closest keyword in the ontology to the attention-weighted word vector. With the suggested methodology, our tracker can overcome various difficulties due to the scarce training data that existing machine learning-based trackers had, such as predicting user intents they have not seen before. We show that our tracker outperforms other trackers submitted to the challenge with respect to most of the performance measures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2072–2082},
numpages = {11}
}

@article{10.1109/TASLP.2018.2859777,
author = {Bao, Junwei and Gong, Yeyun and Duan, Nan and Zhou, Ming and Zhao, Tiejun},
title = {Question Generation With Doubly Adversarial Nets},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2859777},
doi = {10.1109/TASLP.2018.2859777},
abstract = {We study the problem of question generation on a specific domain, where there are no labeled data. To address this problem, we propose a novel neural question generation approach called DoubAN, or doubly adversarial nets, which fully utilizes labeled data from other domains source domains and unlabeled data from the target domain. Learning a DoubAN involves two adversarial procedures between a question generator and two adversaries. One adversary is a domain-classification discriminator DC-Dis, which is designed to help the generator learn domain-general representations of the input text. The other is a question-answering discriminator QA-Dis, which provides more training data with estimated reward scores for generated text-question pairs. We conduct experiments on the SQuAD dataset as target-domain unlabeled data and the NewsQA dataset as source-domain labeled data. Experiment results show that our DoubAN achieves better results than baselines. Compared to model variants, which adopt only DC-Dis or QA-Dis, we find that the DC-Dis and QA-Dis indirectly interact with each other and jointly improve the quality of generated questions on the target domain. Moreover, extensive analysis and discussion prove the reasonableness and effectiveness of our proposed approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2230–2239},
numpages = {10}
}

@article{10.1109/TASLP.2018.2842159,
author = {Wang, DeLiang and Chen, Jitong},
title = {Supervised Speech Separation Based on Deep Learning: An Overview},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2842159},
doi = {10.1109/TASLP.2018.2842159},
abstract = {Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning to supervised speech separation has dramatically accelerated progress and boosted separation performance. This paper provides a comprehensive overview of the research on deep learning based supervised speech separation in the last several years. We first introduce the background of speech separation and the formulation of supervised separation. Then, we discuss three main components of supervised separation: learning machines, training targets, and acoustic features. Much of the overview is on separation algorithms where we review monaural methods, including speech enhancement speech-nonspeech separation, speaker separation multitalker separation, and speech dereverberation, as well as multimicrophone techniques. The important issue of generalization, unique to supervised learning, is discussed. This overview provides a historical perspective on how advances are made. In addition, we discuss a number of conceptual issues, including what constitutes the target source.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1702–1726},
numpages = {25}
}

@article{10.1109/TASLP.2018.2843537,
author = {Xiong, Feifei and Goetze, Stefan and Kollmeier, Birger and Meyer, Bernd T.},
title = {Exploring Auditory-Inspired Acoustic Features for Room Acoustic Parameter Estimation From Monaural Speech},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2843537},
doi = {10.1109/TASLP.2018.2843537},
abstract = {Room acoustic parameters that characterize acoustic environments can help to improve signal enhancement algorithms such as for dereverberation, or automatic speech recognition by adapting models to the current parameter set. The reverberation time RT and the early-to-late reverberation ratio ELR are two key parameters. In this paper, we propose a blind ROom Parameter Estimator ROPE based on an artificial neural network that learns the mapping to discrete ranges of the RT and the ELR from single-microphone speech signals. Auditory-inspired acoustic features are used as neural network input, which are generated by a temporal modulation filter bank applied to the speech time-frequency representation. ROPE performance is analyzed in various reverberant environments in both clean and noisy conditions for both fullband and subband RT and ELR estimations. The importance of specific temporal modulation frequencies is analyzed by evaluating the contribution of individual filters to the ROPE performance. Experimental results show that ROPE is robust against different variations caused by room impulse responses measured versus simulated, mismatched noise levels, and speech variability reflected through different corpora. Compared to state-of-the-art algorithms that were tested in the acoustic characterisation of environments ACE challenge, the ROPE model is the only one that is among the best for all individual tasks RT and ELR estimation from fullband and subband signals. Improved fullband estimations are even obtained by ROPE when integrating speech-related frequency subbands. Furthermore, the model requires the least computational resources with a real time factor that is at least two times faster than competing algorithms. Results are achieved with an average observation window of 3 s, which is important for real-time applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1809–1820},
numpages = {12}
}

@article{10.1109/TASLP.2018.2842156,
author = {Koizumi, Yuma and Niwa, Kenta and Hioka, Yusuke and Kobayashi, Kazunori and Haneda, Yoichi},
title = {DNN-Based Source Enhancement to Increase Objective Sound Quality Assessment Score},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2842156},
doi = {10.1109/TASLP.2018.2842156},
abstract = {We propose a training method for deep neural network DNN based source enhancement to increase objective sound quality assessment OSQA scores such as the perceptual evaluation of speech quality. In many conventional studies, DNNs have been used as a mapping function to estimate time–frequency masks and trained to minimize an analytically tractable objective function such as the mean squared error MSE. Since OSQA scores have been used widely for sound-quality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum MSE to create high-quality output signals. However, since most OSQA scores are not analytically tractable, i.e., they are black boxes, the gradient of the objective function cannot be calculated by simply applying backpropagation. To calculate the gradient of the OSQA-based objective function, we formulated a DNN optimization scheme on the basis of black-box optimization, which is used for training a computer that plays a game. For a black-box-optimization scheme, we adopt the policy gradient method for calculating the gradient on the basis of a sampling algorithm. To simulate output signals using the sampling algorithm, DNNs are used to estimate the probability density function of the output signals that maximize OSQA scores. The OSQA scores are calculated from the simulated output signals, and the DNNs are trained to increase the probability of generating the simulated output signals that achieve high OSQA scores. Through several experiments, we found that OSQA scores significantly increased by applying the proposed method, even though the MSE was not minimized.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1780–1792},
numpages = {13}
}

@article{10.1109/TASLP.2018.2842435,
author = {Winter, Fiete and Wierstorf, Hagen and Hold, Christoph and Kruger, Frank and Raake, Alexander and Spors, Sascha},
title = {Colouration in Local Wave Field Synthesis},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2842435},
doi = {10.1109/TASLP.2018.2842435},
abstract = {Sound field synthesis techniques including wave field synthesis and near-field-compensated higher order ambisonics aim at a physically accurate reproduction of a desired sound field inside an extended listening area. This area is surrounded by loudspeakers individually driven by their respective driving signals. The latter have to be chosen such that the superposition of all emitted sound fields coincides with the desired one. Due to practical limitations, artefacts impair the synthesis accuracy resulting in a perceivable change in timbre. Recently, two approaches to so-called local wave field synthesis were published that enhance the reproduction accuracy in a limited region while allowing stronger artefacts outside. This paper reports on two listening experiments comparing conventional techniques for sound field synthesis with the mentioned approaches. Furthermore, the influence of different parametrizations for local wave field synthesis is investigated. The results show that the enhanced reproduction accuracy in local wave field synthesis leads to a reduction of perceived colouration, if a suitable parametrization is chosen.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1913–1924},
numpages = {12}
}

@article{10.1109/TASLP.2018.2845111,
author = {Huang, Jizhou and Sun, Yaming and Zhang, Wei and Wang, Haifeng and Liu, Ting},
title = {Entity Highlight Generation as Statistical and Neural Machine Translation},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2845111},
doi = {10.1109/TASLP.2018.2845111},
abstract = {Entity highlight refers to a short, concise, and characteristic description for an entity, which can be applied to various applications. In this article, we study the problem of automatically generating entity highlights from the descriptive sentences of entities. Specifically, we develop two computational approaches, one is inspired by the statistical machine translation SMT and another is a sequence-to-sequence learning Seq2Seq approach, which has been successfully applied in neural machine translation and neural summarization. In the Seq2Seq approach, we use attention mechanism, copy mechanism, and coverage mechanism. To generate entity-specific highlights, we also incorporate entity name into the Seq2Seq model to guide the decoding process. We automatically collect large-scale instances as training data without any manual annotation, and ask annotators to create a test set. We compare with several strong baseline methods, and evaluate the approaches with both automatic evaluation and manual evaluation. Experimental results show that the entity enhanced Seq2Seq model with attention, copy, and coverage mechanisms significantly outperforms all other approaches in terms of multiple evaluation metrics.1},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1860–1872},
numpages = {13}
}

@article{10.1109/TASLP.2018.2835719,
author = {Khan, Faheem Ullah and Milner, Ben P. and Le Cornu, Thomas},
title = {Using Visual Speech Information in Masking Methods for Audio Speaker Separation},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2835719},
doi = {10.1109/TASLP.2018.2835719},
abstract = {This paper examines whether visual speech information can be effective within audio-masking-based speaker separation to improve the quality and intelligibility of the target speech. Two visual-only methods of generating an audio mask for speaker separation are first developed. These use a deep neural network to map the visual speech features to an audio feature space from which both visually derived binary masks and visually derived ratio masks are estimated, before application to the speech mixture. Second, an audio ratio masking method forms a baseline approach for speaker separation which is extended to exploit visual speech information to form audio-visual ratio masks. Speech quality and intelligibility tests are carried out on the visual-only, audio-only, and audio-visual masking methods of speaker separation at mixing levels from $-$ 10 to +10 dB. These reveal substantial improvements in the target speech when applying the visual-only and audio-only masks, but with highest performance occurring when combining audio and visual information to create the audio-visual masks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1742–1754},
numpages = {13}
}

@article{10.1109/TASLP.2018.2846402,
author = {Do, Quoc Truong and Sakti, Sakriani and Nakamura, Satoshi},
title = {Sequence-to-Sequence Models for Emphasis Speech Translation},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2846402},
doi = {10.1109/TASLP.2018.2846402},
abstract = {Speech-to-speech translation S2ST systems are capable of breaking language barriers in cross-lingual communication by translating speech across languages. Recent studies have introduced many improvements that allow existing S2ST systems to handle not only linguistic meaning but also paralinguistic information such as emphasis by proposing additional emphasis estimation and translation components. However, the approach used for emphasis translation is not optimal for sequence translation tasks and fails to easily handle the long-term dependencies of words and emphasis levels. It also requires the quantization of emphasis levels and treats them as discrete labels instead of continuous values. Moreover, the whole translation pipeline is fairly complex and slow because all components are trained separately without joint optimization. In this paper, we make two contributions: 1 we propose an approach that can handle continuous emphasis levels based on sequence-to-sequence models, and 2 we combine machine and emphasis translation into a single model, which greatly simplifies the translation pipeline and make it easier to perform joint optimization. Our results on an emphasis translation task indicate that our translation models outperform previous models by a large margin in both objective and subjective tests. Experiments on a joint translation model also show that our models can perform joint translation of words and emphasis with one-word delays instead of full-sentence delays while preserving the translation performance of both tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1873–1883},
numpages = {11}
}

@article{10.1109/TASLP.2018.2837384,
author = {Senel, Lutfi Kerem and Utlu, Ihsan and Yucesoy, Veysel and Koc, Aykut and Cukur, Tolga},
title = {Semantic Structure and Interpretability of Word Embeddings},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2837384},
doi = {10.1109/TASLP.2018.2837384},
abstract = {Dense word embeddings, which encode meanings of words to low-dimensional vector spaces, have become very popular in natural language processing NLP research due to their state-of-the-art performances in many NLP tasks. Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions making interpretation of dimensions a big challenge. In this study, we propose a statistical method to uncover the underlying latent semantic structure in the dense word embeddings. To perform our analysis, we introduce a new dataset SEMCAT that contains more than 6500 words semantically grouped under 110 categories. We further propose a method to quantify the interpretability of the word embeddings. The proposed method is a practical alternative to the classical word intrusion test that requires human intervention.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1769–1779},
numpages = {11}
}

@article{10.1109/TASLP.2018.2839362,
author = {Li, Xiaofei and Gannot, Sharon and Girin, Laurent and Horaud, Radu},
title = {Multichannel Identification and Nonnegative Equalization for Dereverberation and Noise Reduction Based on Convolutive Transfer Function},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2839362},
doi = {10.1109/TASLP.2018.2839362},
abstract = {This paper addresses the problems of blind multichannel identification and equalization for joint speech dereverberation and noise reduction. The time-domain cross-relation method is hardly applicable for blind room impulse response identification due to the near-common zeros of the long impulse responses. We extend the cross-relation method to the short-time Fourier transform STFT domain, in which the time-domain impulse response is approximately represented by the convolutive transfer function CTF with much less coefficients. For the oversampled STFT, CTFs suffer from the common zeros caused by the nonflat frequency response of the STFT window. To overcome this, we propose to identify CTFs using the STFT framework with oversampled signals and critically sampled CTFs, which is a good tradeoff between the frequency aliasing of the signals and the common zeros problem of CTFs. The identified complex-valued CTFs are not accurate enough for multichannel equalization due to the frequency aliasing of the CTFs. Hence, we only use the CTF magnitudes, which leads to a nonnegative multichannel equalization method based on a nonnegative convolution model between the STFT magnitude of the source signal and the CTF magnitude. Compared with the complex-valued convolution model, this nonnegative convolution model is shown to be more robust against the CTF perturbations. To recover the STFT magnitude of the source signal and to reduce the additive noise, the $ell _2$-norm fitting error between the STFT magnitude of the microphone signals and the nonnegative convolution is constrained to be less than a noise power related tolerance. Meanwhile, the $ell _1$ -norm of the STFT magnitude of the source signal is minimized to impose the sparsity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1755–1768},
numpages = {14}
}

@article{10.1109/TASLP.2018.2847442,
author = {Fontana, Federico and Bozzo, Enrico},
title = {Explicit Fixed-Point Computation of Nonlinear Delay-Free Loop Filter Networks},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2847442},
doi = {10.1109/TASLP.2018.2847442},
abstract = {An iterative method is proposed for the explicit computation of discrete-time nonlinear filter networks containing delay-free loops. The method relies on a fixed-point search of the signal values at every temporal step. The formal as well as numerical properties of fixed-point solvers delimit its applicability: On the one hand, the method allows for a reliable prediction of the frequency rates where the simulation is stable, while, on the other hand, its straightforward applicability is counterbalanced by low speed of convergence. Especially in presence of specific nonlinear characteristics, the use of a fixed-point search is limited if the real-time constraint holds. For this reason, the method becomes useful especially during the digital model prototyping stage, as exemplified while revisiting a previous discrete-time realization of the voltage-controlled filter aboard the EMS VCS3 analog synthesizer. Further tests conducted on a digital ring modulator model support the above considerations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1884–1896},
numpages = {13}
}

@article{10.1109/TASLP.2018.2837223,
author = {Wang, Rui and Utiyama, Masao and Finch, Andrew and Liu, Lemao and Chen, Kehai and Sumita, Eiichiro},
title = {Sentence Selection and Weighting for Neural Machine Translation Domain Adaptation},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2837223},
doi = {10.1109/TASLP.2018.2837223},
abstract = {Neural machine translation NMT has been prominent in many machine translation tasks. However, in some domain-specific tasks, only the corpora from similar domains can improve translation performance. If out-of-domain corpora are directly added into the in-domain corpus, the translation performance may even degrade. Therefore, domain adaptation techniques are essential to solve the NMT domain problem. Most existing methods for domain adaptation are designed for the conventional phrase-based machine translation. For NMT domain adaptation, there have been only a few studies on topics such as fine tuning, domain tags, and domain features. In this paper, we have four goals for sentence level NMT domain adaptation. First, the NMT's internal sentence embedding is exploited and the sentence embedding similarity is used to select out-of-domain sentences that are close to the in-domain corpus. Second, we propose three sentence weighting methods, i.e., sentence weighting, domain weighting, and batch weighting, to balance the data distribution during NMT training. Third, in addition, we propose dynamic training methods to adjust the sentence selection and weighting during NMT training. Fourth, to solve the multidomain problem in a real-world NMT scenario where the domain distributions of training and testing data often mismatch, we proposed a multidomain sentence weighting method to balance the domain distributions of training data and match the domain distributions of training and testing data. The proposed methods are evaluated in international workshop on spoken language translation IWSLT English-to-French/German tasks and a multidomain English-to-French task. Empirical results show that the sentence selection and weighting methods can significantly improve the NMT performance, outperforming the existing baselines.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1727–1741},
numpages = {15}
}

@article{10.1109/TASLP.2018.2845121,
author = {Wu, Kai and Reju, Vaninirappuputhenpurayil Gopalan and Khong, Andy W. H.},
title = {Multisource DOA Estimation in a Reverberant Environment Using a Single Acoustic Vector Sensor},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2845121},
doi = {10.1109/TASLP.2018.2845121},
abstract = {We address the problem of direction-of-arrival DOA estimation for multiple speech sources in an enclosed environment using a single acoustic vector sensor. The challenges in such scenario include reverberation and overlapping of the source signals. In this work, we exploit low-reverberant-single-source LRSS points in the time–frequency TF domain, where a particular source is dominant with high signal-to-reverberation ratio. Unlike conventional algorithms having limitation that such potential points need to be detected at “TF-zone” level, the proposed algorithm performs LRSS detection at “TF-point” level. Therefore, for the proposed algorithm, the potential LRSS points need not be neighbors of each other within a TF zone to be detected, resulting an increased number of detected LRSS points. The detected LRSS points are further screened by an outlier removal step such that only reliable LRSS points will be used for DOA estimation. Simulations and experiments were conducted to demonstrate the effectiveness of the proposed algorithm in multisource reverberant environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1848–1859},
numpages = {12}
}

@article{10.1109/TASLP.2018.2845665,
author = {Xue, Wei and Moore, Alastair. H. and Brookes, Mike and Naylor, Patrick A.},
title = {Modulation-Domain Multichannel Kalman Filtering for Speech Enhancement},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2845665},
doi = {10.1109/TASLP.2018.2845665},
abstract = {Compared with single-channel speech enhancement methods, multichannel methods can utilize spatial information to design optimal filters. Although some filters adaptively consider second-order signal statistics, the temporal evolution of the speech spectrum is usually neglected. By using linear prediction LP to model the inter-frame temporal evolution of speech, single-channel Kalman filtering KF based methods have been developed for speech enhancement. In this paper, we derive a multichannel KF MKF that jointly uses both interchannel spatial correlation and interframe temporal correlation for speech enhancement. We perform LP in the modulation domain, and by incorporating the spatial information, derive an optimal MKF gain in the short-time Fourier transform domain. We show that the proposed MKF reduces to the conventional multichannel Wiener filter if the LP information is discarded. Furthermore, we show that, under an appropriate assumption, the MKF is equivalent to a concatenation of the minimum variance distortion response beamformer and a single-channel modulation-domain KF and therefore present an alternative implementation of the MKF. Experiments conducted on a public head-related impulse response database demonstrate the effectiveness of the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1833–1847},
numpages = {15}
}

@article{10.1109/TASLP.2018.2842146,
author = {Paleologu, Constantin and Benesty, Jacob and Ciochina, Silviu},
title = {Linear System Identification Based on a Kronecker Product Decomposition},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2842146},
doi = {10.1109/TASLP.2018.2842146},
abstract = {Linear system identification is a key problem in many important applications, among which echo cancelation is a very challenging one. Due to the long length impulse responses i.e., echo paths to be identified, there is always room and needs to improve the performance of the echo cancelers, especially in terms of complexity, convergence rate, robustness, and accuracy. In this paper, we propose a new way to address the system identification problem from the echo cancelation perspective, by exploiting an optimal approximation of the impulse response based on the nearest Kronecker product decomposition. Also, we make a first step toward this direction, by developing an iterative Wiener filter based on this approach. As compared to the conventional Wiener filter, the proposed solution is much more attractive since its gain is twofold. First, the matrices to be inverted or, preferably, linear systems to be solved are smaller as compared to the conventional approach. Second, as a consequence, the iterative Wiener filter leads to a good estimate of the impulse response, even when a small amount of data is available for the estimation of the statistics. Simulation results support the theoretical findings and indicate the good results of the proposed approach, for the identification of different network and acoustic impulse responses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1793–1808},
numpages = {16}
}

@article{10.1109/TASLP.2018.2844025,
author = {Le Lan, Gael and Charlet, Delphine and Larcher, Anthony and Meignier, Sylvain},
title = {An Adaptive Method for Cross-Recording Speaker Diarization},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2844025},
doi = {10.1109/TASLP.2018.2844025},
abstract = {Nowadays, state-of-the-art speaker diarization systems heavily rely on between-recording variability compensation methods to accurately process large collections of recordings. Variability estimation is performed on consequent training datasets, which must be labeled by speaker. One major problem of such systems is the acoustic mismatch between training and target data that degrades performances. Most of the collections contain lots of speakers speaking in various acoustic conditions. In this paper, we investigate how unlabeled speakers can help improve between-recording variability estimation, to overcome the mismatch issue. We propose a scalable unsupervised adaptation framework for two types of variability compensation. The proposed framework consists in adapting a state-of-the-art diarization and linking system, trained on out-of-domain data, using the data of the collection itself. Experiments in mismatch condition are run on two French Television shows, while the initial training dataset is composed of Radio recordings. Results indicate that the proposed adaptation framework reduces the cross-recording DER of 13% in average for variable collection sizes.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1821–1832},
numpages = {12}
}

@article{10.1109/TASLP.2018.2847459,
author = {Andersen, Asger Heidemann and de Haan, Jan Mark and Tan, Zheng-Hua and Jensen, Jesper},
title = {Nonintrusive Speech Intelligibility Prediction Using Convolutional Neural Networks},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2847459},
doi = {10.1109/TASLP.2018.2847459},
abstract = {Speech Intelligibility Prediction SIP algorithms are becoming popular tools within the development and operation of speech processing devices and algorithms. However, many SIP algorithms require knowledge of the underlying clean speech; a signal that is often not available in real-world applications. This has led to increased interest in nonintrusive SIP algorithms, which do not require clean speech to make predictions. In this paper, we investigate the use of Convolutional Neural Networks CNNs for nonintrusive SIP. To do so, we utilize a CNN architecture that shows similarities to existing SIP algorithms, in terms of computational structure, and which allows for easy and meaningful visualization and interpretation of trained weights. We evaluate this architecture using a large dataset obtained by combining datasets from the literature. The proposed method shows high prediction performance when compared with four existing intrusive and nonintrusive SIP algorithms. This demonstrates the potential of deep learning for speech intelligibility prediction.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1925–1939},
numpages = {15}
}

@article{10.1109/TASLP.2018.2839355,
author = {Widmark, Simon},
title = {Causal IIR Audio Precompensator Filters Subject to Quadratic Constraints},
year = {2018},
issue_date = {October 2018},
publisher = {IEEE Press},
volume = {26},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2839355},
doi = {10.1109/TASLP.2018.2839355},
abstract = {Infinite impulse response IIR Wiener precompensator design, with constraints on causality, is here also extended to incorporate general quadratic constraints. A method for finding a linear quadratic optimal, causal discrete-time multiple-input multiple-output filter subject to a set of user defined constraints is proposed and analyzed. A method for designing causal filters subject to constraints on the power gains in a large number of small frequency intervals is also proposed. The resulting set of methods provide constrained stable IIR filters with optimal parameterization. Compared to finite impulse response Wiener filtering, the computational complexity is much lower; and compared to noncausal frequency domain designs, we gain control of the time-domain properties of the compensated system. The design methods are applied to a room compensation audio problem subject to filter power gain constraints and are compared to a corresponding noncausal per-frequency method. The results are presented with audio filtering and sound field control as main motivating applications but the methods extend to other areas of linear feedforward controller design and Wiener filtering.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1897–1912},
numpages = {16}
}

@article{10.1109/TASLP.2018.2827300,
author = {Menzies, Dylan and Fazi, Filippo Maria},
title = {A Complex Panning Method for Near-Field Imaging},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2827300},
doi = {10.1109/TASLP.2018.2827300},
abstract = {Conventional amplitude panning can be used to produce images of distant objects. An extended panning method is presented that can also produce image cues for the near-field region, by the control of Inter-aural Level Difference cues in the low frequency range below ∼1000 Hz. The approach has grown from an adaptive panning method that corrects for the dependence of image direction on head orientation. Stereo panning functions are derived from a general formulation. A single first order filter is required for each image. The method is tested by simulating a range of configurations using measured Head Related Transfer Functions, and also with listening tests. The results confirm the ability of the method to control near-field cues, while also compensating image direction for head rotation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1539–1548},
numpages = {10}
}

@article{10.1109/TASLP.2018.2828321,
author = {Evers, Christine and Naylor, Patrick A.},
title = {Acoustic SLAM},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2828321},
doi = {10.1109/TASLP.2018.2828321},
abstract = {An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to, and interact with sound sources of interest. Acoustic scene mapping creates a three-dimensional 3D representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival DoA estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moreover, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping aSLAM, which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources while passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1484–1498},
numpages = {15}
}

@article{10.1109/TASLP.2018.2834729,
author = {Xiao, Ke and Wang, Supin and Wan, Mingxi and Wu, Liang},
title = {Radiated Noise Suppression for Electrolarynx Speech Based on Multiband Time-Domain Amplitude Modulation},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2834729},
doi = {10.1109/TASLP.2018.2834729},
abstract = {Radiated noise severely degrades the electrolarynx EL speech. It cannot be thoroughly suppressed by conventional frequency-domain enhancement methods. In this paper, a new method, called multiband time-domain amplitude modulation MTAM, is proposed to reduce the radiated noise of EL speech. In the proposed method, the speech components changing slowly that represent the radiated noise are removed by directly modulating the time-domain amplitudes in multiple frequency bands. The EL speech enhanced by the proposed MTAM and the conventional frequency-domain enhancement methods spectral subtraction and Wiener filtering are evaluated on both acoustic and perceptual characteristics. The acoustic analysis reveals that the MTAM not only can reduce the radiated noise more thoroughly but can also easily control the residual noise intensity by adjusting a modulation parameter $lambda $. Moreover, the MTAM can avoid causing new artificial noise that cannot be avoided by the conventional frequency-domain enhancement methods. The perceptual analysis indicates that the MTAM also have better performance on increasing the acceptability and the consonant intelligibility of EL speech than spectral subtraction and Wiener filtering. These findings validate that the MTAM indeed works well in suppressing the radiated noise of EL speech and avoiding the artificial noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1585–1593},
numpages = {9}
}

@article{10.5555/3232296.3232304,
author = {Misra, Abhinav and Hansen, John H. L.},
title = {Maximum-Likelihood Linear Transformation for Unsupervised Domain Adaptation in Speaker Verification},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
abstract = {Recent advances in front-end factor analysis through development of i-Vectors have led to significant gains in speaker recognition technology. However, the problem of mismatch between the domains of system development and evaluation data remains a challenging one. This domain mismatch occurs primarily because of the variability in the sources of development and evaluation data. In this study, we propose a novel method of unsupervised probabilistic feature transformation UPFT to reduce this domain mismatch by transforming an out-of-domain development data toward in-domain development data. We formulate the alignment of two different domains as a probability density estimation problem. We first train a Gaussian mixture model GMM using the out-of-domain i-Vectors. Next, we employ an expectation–maximization EM algorithm to fit the means of the GMM to the in-domain i-Vectors by maximizing the overall likelihood. At the optimum, the two domains become closer to each other in the i-Vector space. While reaching the optimum through multiple iterations of the EM, we reparameterize the centroid locations using the following set of transformation parameters: rotation, translation, and scaling. These transformation parameters, which are obtained during the optimization process, are later used to transform the out-of-domain i-Vectors toward in-domain i-Vectors. We observe that such a transformation leads to an improvement in performance of the out-of-domain speaker recognition system. Our proposed method has an added advantage of being completely unsupervised, and thus does not rely on any tuning parameters. We conduct experiments on both 2013 domain adaptation challenge corpus as well as National Institute of Standards and Technology Speaker Recognition Evaluation SRE—2016 corpus. On both corpora, we obtain significant improvements using the proposed UPFT solution. Specifically for the SRE-2016 corpus, using a cosine distance scoring based system, we are able to recover almost 90% of the performance gap between an in-domain and out-of-domain system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1549–1558},
numpages = {10}
}

@article{10.1109/TASLP.2018.2830117,
author = {Zhang, Meishan and Yu, Nan and Fu, Guohong},
title = {A Simple and Effective Neural Model for Joint Word Segmentation and POS Tagging},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2830117},
doi = {10.1109/TASLP.2018.2830117},
abstract = {Joint models have shown stronger capabilities for Chinese word segmentation and POS tagging, and have received great interests in the community of Chinese natural language processing. In this paper, we follow this line of work, presenting a simple yet effective sequence-to-sequence neural model for the joint task, based on a well-defined transition system, by using long short term memory neural network structures. We conduct experiments on five different datasets. The results demonstrate that our proposed model is highly competitive. By using well-trained character-level embeddings, the proposed neural joint model is able to obtain the best-reported performances in the literature.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1528–1538},
numpages = {11}
}

@article{10.1109/TASLP.2018.2830105,
author = {Carabias-Orti, Julio Jose and Nikunen, Joonas and Virtanen, Tuomas and Vera-Candeas, Pedro},
title = {Multichannel Blind Sound Source Separation Using Spatial Covariance Model With Level and Time Differences and Nonnegative Matrix Factorization},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2830105},
doi = {10.1109/TASLP.2018.2830105},
abstract = {This paper presents an algorithm for multichannel sound source separation using explicit modeling of level and time differences in source spatial covariance matrices SCM. We propose a novel SCM model in which the spatial properties are modeled by the weighted sum of direction of arrival DOA kernels. DOA kernels are obtained as the combination of phase and level difference covariance matrices representing both time and level differences between microphones for a grid of predefined source directions. The proposed SCM model is combined with the NMF model for the magnitude spectrograms. Opposite to other SCM models in the literature, in this work, source localization is implicitly defined in the model and estimated during the signal factorization. Therefore, no localization preprocessing is required. Parameters are estimated using complex-valued nonnegative matrix factorization with both Euclidean distance and Itakura–Saito divergence. Separation performance of the proposed system is evaluated using the two-channel SiSEC development dataset and four channels signals recorded in a regular room with moderate reverberation. Finally, a comparison to other state-of-the-art methods is performed, showing better achieved separation performance in terms of SIR and perceptual measures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1512–1527},
numpages = {16}
}

@article{10.1109/TASLP.2018.2836143,
author = {Mahe, Gael and Jaidane, Meriem},
title = {Perceptually Controlled Reshaping of Sound Histograms},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2836143},
doi = {10.1109/TASLP.2018.2836143},
abstract = {Many audio processing algorithms have optimal performance for specific signal statistical distributions that may not be fulfilled for all signals. When the original signal is available, we propose to add an inaudible noise so that the distribution of the signal-plus-noise mixture is as close as possible to a given target distribution. The proposed generic algorithm independent from the application adds iteratively a low-power white noise to a flat-spectrum version of the signal, until the target distribution or the noise audibility is reached. The latter is assessed through a frequency masking model. Two implementations of this sound reshaping are described, according to the level of the targeted transformation and to the foreseen application: histogram global reshaping HGR to change the global shape of the histogram and histogram local reshaping HLR to locally “chisel” the histogram, but keeping the global shape unchanged. These two variants are illustrated by two applications, where the inaudibility of the noise generated by the algorithm is required: “sparsification” for source separation and low-pass filtering of the histogram for application of the quantization theorem, respectively. In both cases, the target histogram is reached or almost reached, and the transformation is inaudible. The experiments show that the source separation performs better with HGR and that the HLR allows a better application of the quantization theorem.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1671–1683},
numpages = {13}
}

@article{10.1109/TASLP.2018.2835720,
author = {Airaksinen, Manu and Juvela, Lauri and Bollepalli, Bajibabu and Yamagishi, Junichi and Alku, Paavo},
title = {A Comparison Between STRAIGHT, Glottal, and Sinusoidal Vocoding in Statistical Parametric Speech Synthesis},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2835720},
doi = {10.1109/TASLP.2018.2835720},
abstract = {A vocoder is used to express a speech waveform with a controllable parametric representation that can be converted back into a speech waveform. Vocoders representing their main categories mixed excitation, glottal, and sinusoidal vocoders were compared in this study with formal and crowd-sourced listening tests. The vocoder quality was measured within the context of analysis–synthesis as well as text-to-speech TTS synthesis in a modern statistical parametric speech synthesis framework. Furthermore, the TTS experiments were divided into synthesis with vocoder-specific features and synthesis with a shared envelope model, where the waveform generation method of the vocoders is mainly responsible for the quality differences. Finally, all of the tests included four distinct voices as a way to investigate the effect of different speakers on the synthesized speech quality. The obtained results suggest that the choice of the voice has a profound impact on the overall quality of the vocoder-generated speech, and the best vocoder for each voice can vary case by case. The single best-rated TTS system was obtained with the glottal vocoder GlottDNN using a male voice with low expressiveness. However, the results indicate that the sinusoidal vocoder PML pulse model in log-domain has the best overall performance across the performed tests. Finally, when controlling for the spectral models of the vocoders, the observed differences are similar to the baseline results. This indicates that the waveform generation method of a vocoder is essential for quality improvements.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1658–1670},
numpages = {13}
}

@article{10.1109/TASLP.2018.2831456,
author = {Zhang, Chunlei and Koishida, Kazuhito and Hansen, John H. L.},
title = {Text-Independent Speaker Verification Based on Triplet Convolutional Neural Network Embeddings},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2831456},
doi = {10.1109/TASLP.2018.2831456},
abstract = {The effectiveness of introducing deep neural networks into conventional speaker recognition pipelines has been broadly shown to benefit system performance. A novel text-independent speaker verification SV framework based on the triplet loss and a very deep convolutional neural network architecture i.e., Inception-Resnet-v1 are investigated in this study, where a fixed-length speaker discriminative embedding is learned from sparse speech features and utilized as a feature representation for the SV tasks. A concise description of the neural network based speaker discriminative training with triplet loss is presented. An Euclidean distance similarity metric is applied in both network training and SV testing, which ensures the SV system to follow an end-to-end fashion. By replacing the final max/average pooling layer with a spatial pyramid pooling layer in the Inception-Resnet-v1 architecture, the fixed-length input constraint is relaxed and an obvious performance gain is achieved compared with the fixed-length input speaker embedding system. For datasets with more severe training/test condition mismatches, the probabilistic linear discriminant analysis PLDA back end is further introduced to replace the distance based scoring for the proposed speaker embedding system. Thus, we reconstruct the SV task with a neural network based front-end speaker embedding system and a PLDA that provides channel and noise variabilities compensation in the back end. Extensive experiments are conducted to provide useful hints that lead to a better testing performance. Comparison with the state-of-the-art SV frameworks on three public datasets i.e., a prompt speech corpus, a conversational speech Switchboard corpus, and NIST SRE10 10 s–10 s condition justifies the effectiveness of our proposed speaker embedding system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1633–1644},
numpages = {12}
}

@article{10.1109/TASLP.2018.2830113,
author = {Wu, Chih-Wei and Dittmar, Christian and Southall, Carl and Vogl, Richard and Widmer, Gerhard and Hockman, Jason and Muller, Meinard and Lerch, Alexander},
title = {A Review of Automatic Drum Transcription},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2830113},
doi = {10.1109/TASLP.2018.2830113},
abstract = {In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often defining the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classification of drum sound events by computational methods is considered to be an important and challenging research problem in the broader field of music information retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term automatic drum transcription ADT. This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on non-negative matrix factorization and recurrent neural networks. We explain the methods’ technical details and drum-specific variations and evaluate these approaches on publicly available data sets with a consistent experimental setup. Finally, the open issues and underexplored areas in ADT research are identified and discussed, providing future directions in this field.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1457–1483},
numpages = {27}
}

@article{10.1109/TASLP.2018.2830116,
author = {Laroche, Clement and Kowalski, Matthieu and Papadopoulos, Helene and Richard, Gael},
title = {Hybrid Projective Nonnegative Matrix Factorization With Drum Dictionaries for Harmonic/Percussive Source Separation},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2830116},
doi = {10.1109/TASLP.2018.2830116},
abstract = {One of the most general models of music signals considers that such signals can be represented as a sum of two distinct components: a tonal part that is sparse in frequency and temporally stable and a transient or percussive part that is composed of short-term broadband sounds. In this paper, we propose a novel hybrid method built upon nonnegative matrix factorization NMF that decomposes the time frequency representation of an audio signal into such two components. The tonal part is estimated by a sparse and orthogonal nonnegative decomposition, and the transient part is estimated by a straightforward NMF decomposition constrained by a pre-learned dictionary of smooth spectra. The optimization problem at the heart of our method remains simple with very few hyperparameters and can be solved thanks to simple multiplicative update rules. The extensive benchmark on a large and varied music database against four state of the art harmonic/percussive source separation algorithms demonstrate the merit of the proposed approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1499–1511},
numpages = {13}
}

@article{10.5555/3232296.3232303,
author = {Wakabayashi, Yukoh and Fukumori, Takahiro and Nakayama, Masato and Nishiura, Takanobu and Yamashita, Yoichi},
title = {Single-Channel Speech Enhancement With Phase Reconstruction Based on Phase Distortion Averaging},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
abstract = {Speech enhancement has been widely investigated for several decades, but by modifying only the amplitude spectrum of a speech signal, ignoring the phase spectrum, which has been regarded as an unimportant feature. However, it was recently reported that the phase spectrum plays an important role in speech quality and intelligibility. In this paper, we propose a phase reconstruction method based on harmonic enhancement using the fundamental frequency and phase distortion feature. This feature is known to show fluctuations in the phase spectrum with respect to time and frequency. We estimate the speech phase spectrum by considering the relationship between harmonic phase spectra. Experimental evaluations indicate that the proposed phase reconstruction method improves speech quality in various noisy environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1559–1569},
numpages = {11}
}

@article{10.1109/TASLP.2018.2834733,
author = {MV, Achuth Rao and Ghosh, Prasanta Kumar},
title = {PSFM—A Probabilistic Source Filter Model for Noise Robust Glottal Closure Instant Detection},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2834733},
doi = {10.1109/TASLP.2018.2834733},
abstract = {Accurate estimation of glottal closure instant GCI enables several pitch synchronous speech analysis, such as prosody modifications, glottal inverse filtering, and study of pathological speech. We propose a probabilistic source-filter model PSFM for voiced speech, where the source is modeled using the Bernoulli Gaussian distribution, which models the GCI locations and the all-pole filter coefficients are modeled using Gaussian distribution. The probability of GCIs at each speech sample is estimated using the Gibbs sampling. We propose a cost to estimate the exact GCI locations using the N-best dynamic programming. A key feature of the proposed PSFM is that it allows us to include the second-order statistics of the noise for estimating the GCI locations, thereby resulting in a noise robust GCI detection technique, although it has high computational complexity. Evaluation on archivable priority list actual-word database APLAWD database shows the proposed algorithm performs at par with the state-of-the-art GCI detection method on clean speech. However, when evaluated in noisy conditions using five types of noises at six different signal-to-noise ratio SNR levels, we observe that the proposed method performs better than the best of the existing GCI detection scheme, particularly at low SNR condition indicating the noise robustness of the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1645–1657},
numpages = {13}
}

@article{10.5555/3232296.3232307,
author = {Fahim, Abdullah and Samarasinghe, Prasanga N. and Abhayapala, Thushara D.},
title = {PSD Estimation and Source Separation in a Noisy Reverberant Environment Using a Spherical Microphone Array},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
abstract = {In this paper, we propose an efficient technique for estimating individual power spectral density PSD components, i.e., PSD of each desired sound source as well as of noise and reverberation, in a multisource reverberant sound scene with coherent background noise. We formulate the problem in the spherical harmonics domain to take the advantage of the inherent orthogonality of the spherical harmonics basis functions and extract the PSD components from the cross-correlation between the different sound field modes. We also investigate an implementation issue that occurs at the nulls of the Bessel functions and offer an engineering solution. The performance evaluation takes place in a practical environment with a commercial microphone array in order to measure the robustness of the proposed algorithm against all the deviations incurred in practice. We also exhibit an application of the proposed PSD estimator through a source septation algorithm and compare the performance with a contemporary method in terms of different objective measures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1594–1607},
numpages = {14}
}

@article{10.1109/TASLP.2018.2821903,
author = {Fu, Szu-Wei and Wang, Tao-Wei and Tsao, Yu and Lu, Xugang and Kawai, Hisashi},
title = {End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2821903},
doi = {10.1109/TASLP.2018.2821903},
abstract = {Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in the existing literature, there is an inconsistency between the model optimization criterion and the evaluation criterion for the enhanced speech. For example, in measuring speech intelligibility, most of the evaluation metric is based on a short-time objective intelligibility STOI measure, while the frame based mean square error MSE between estimated and clean speech is widely used in optimizing the model. Due to the inconsistency, there is no guarantee that the trained model can provide optimal performance in applications. In this study, we propose an end-to-end utterance-based speech enhancement framework using fully convolutional neural networks FCN to reduce the gap between the model optimization and the evaluation criterion. Because of the utterance-based optimization, temporal correlation information of long speech segments, or even at the entire utterance level, can be considered to directly optimize perception-based objective functions. As an example, we implemented the proposed FCN enhancement framework to optimize the STOI measure. Experimental results show that the STOI of a test speech processed by the proposed approach is better than conventional MSE-optimized speech due to the consistency between the training and the evaluation targets. Moreover, by integrating the STOI into model optimization, the intelligibility of human subjects and automatic speech recognition system on the enhanced speech is also substantially improved compared to those generated based on the minimum MSE criterion.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1570–1584},
numpages = {15}
}

@article{10.5555/3232296.3232314,
author = {Zhang, Weiwei and Chen, Zhe and Yin, Fuliang and Zhang, Qiaoling},
title = {Melody Extraction From Polyphonic Music Using Particle Filter and Dynamic Programming},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
abstract = {Melody extraction from polyphonic music is one important but challenging task in the music information retrieval community. In this paper, a new melody extraction method based on the particle filter and dynamic programming is proposed. The constant-Q transform is first introduced for multiresolution spectral analysis of polyphonic music. Then, the melody extraction is modeled in the Bayesian filtering framework, and the particle filter is used to get a rough melody contour. Specially, the pitch transition probability of adjacent frames is approximated according to the statistical analysis based on one publicly available dataset, and the likelihood of frame-wise pitches is defined by considering pitch salience, spectral smoothness, and timbre similarity. After that, the preliminary melodic contour obtained by particle filter is smoothed to achieve the frame-wise pitch range limitation. Finally, the dynamic programming is used to accurately track the final melodic contour. The proposed method requires no prior information, and is suitable for both instrumental and vocal melodies. The experimental results show that the performances of the proposed method is robust among four publicly available datasets comparing with the state-of-the-art methods, and it achieves the highest averaged raw pitch accuracy and raw chroma accuracy performances with lower octave errors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1620–1632},
numpages = {13}
}

@article{10.1109/TASLP.2018.2836436,
author = {Huang, Qinghua and Zhang, Lin and Fang, Yong},
title = {Two-Step Spherical Harmonics ESPRIT-Type Algorithms and Performance Analysis},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2836436},
doi = {10.1109/TASLP.2018.2836436},
abstract = {Spherical arrays have been widely used in direction-of-arrival DOA estimation in recent years, and the high-resolution estimation of signal parameter via rotational invariance technique ESPRIT was developed in the spherical harmonics domain. However, the spherical harmonics ESPRIT SHESPRIT cannot estimate the DOA when the elevation approaches 90°. To solve this problem, we present a two-step SHESPRIT TS-SHESPRIT based on two new recurrence relations of complex spherical harmonics. Furthermore, we develop a real-valued two-step SHESPRIT RTS-SHESPRIT that exploits a unitary matrix to obtain a real-valued relation between the signal subspace and the steering matrix to further reduce the computational complexity. However, the number of sources that are estimated by RTS-SHESPRIT is limited. Therefore, we propose the semi-RTS-SHESPRIT method, which reduces the computational complexity associated with eigenvalue decomposition EVD and avoids the limitations of RTS-SHESPRIT. Relative to SHESPRIT and TS-SHESPRIT, RTS-SHESPRIT and semi-RTS-SHESPRIT reduce the computational burden by 75% during EVD. Furthermore, we derive the mean square errors MSEs of the above algorithms and significantly simplify the MSE expressions. Different expressions for the MSEs are due to different recurrence relations used by different SHESPRIT-type algorithms. All proposed two-step SHESPRIT-type algorithms have higher accuracy than traditional SHESPRIT. The simulation results demonstrate the satisfactory performance of our methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1684–1697},
numpages = {14}
}

@article{10.1109/TASLP.2018.2835729,
author = {He, Hongsen and Chen, Jingdong and Benesty, Jacob and Yang, Tao},
title = {Noise Robust Frequency-Domain Adaptive Blind Multichannel Identification With $\ell _p$-Norm Constraint},
year = {2018},
issue_date = {September 2018},
publisher = {IEEE Press},
volume = {26},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2835729},
doi = {10.1109/TASLP.2018.2835729},
abstract = {Blind multichannel identification is a challenging problem in many domains. The normalized multichannel frequency-domain least-mean-square NMCFLMS algorithm was developed to blindly identify a single-input multiple-output acoustic system, which can yield good performance in noise-free environments. However, the robustness of this algorithm to noise has been shown to be problematic. One way to improve the robustness is by applying a constraint on the spectral flatness of the channel impulse responses, which led to the development of the so-called robust normalized multichannel frequency-domain least-mean-square RNMCFLMS algorithm. This spectral flatness constraint, however, may not be always proper or reasonable in realistic acoustic environments. In this paper, we develop an $ell _p$-norm constraint based robust normalized multichannel frequency-domain least-mean-square $ell _p$-RNMCFLMS algorithm. The $ell _p$ -norm constraint is introduced into the NMCFLMS algorithm to control the effect of different $ell _p$-norm penalties on the adaptive filter for the impulse responses with different degrees of sparseness. Numerical and realistic experiments justify the effectiveness of the proposed $ell _p$-RNMCFLMS algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1608–1619},
numpages = {12}
}

@article{10.1109/TASLP.2018.2828980,
author = {Valentini-Botinhao, Cassia and Yamagishi, Junichi},
title = {Speech Enhancement of Noisy and Reverberant Speech for Text-to-Speech},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2828980},
doi = {10.1109/TASLP.2018.2828980},
abstract = {Text-to-speech voices created from noisy and reverberant recordings are of lower quality. A simple way to improve this is to increase the quality of the recordings prior to text-to-speech training with speech enhancement methods such as noise suppression and dereverberation. In this paper, we opted for this approach and to perform the enhancement, we used a recurrent neural network. The network is trained with parallel data of clean and lower quality recordings of speech. The lower quality data was artificially created by adding recordings of environmental noise to studio-quality recordings of speech and by convolving room impulse responses with these clean recordings. We trained separate networks with noise-only, reverberation-only, and both reverberation and additive noise data. The quality of voices trained with lower quality data that has been enhanced using these networks was significantly higher in all cases. For the noise-only case, the enhanced synthetic voice ranked as high as the voice trained with clean data. For the most realistic and challenging scenario, when both noise and reverberation were present, the improvements were more modest, but still significant.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1420–1433},
numpages = {14}
}

@article{10.1109/TASLP.2018.2829405,
author = {Koutrouvelis, Andreas I. and Sherson, Thomas W. and Heusdens, Richard and Hendriks, Richard C.},
title = {A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks With Arbitrary Topology},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2829405},
doi = {10.1109/TASLP.2018.2829405},
abstract = {We propose a new robust distributed linearly constrained beamformer that utilizes a set of linear equality constraints to reduce the cross power spectral density matrix to a block-diagonal form. The proposed beamformer has a convenient objective function for use in arbitrary distributed network topologies while having identical performance to a centralized implementation. Moreover, the new optimization problem is robust to relative acoustic transfer function RATF estimation errors and to target activity detection TAD errors. Two variants of the proposed beamformer are presented and evaluated in the context of multimicrophone speech enhancement in a wireless acoustic sensor network, and are compared with other state-of-the-art distributed beamformers in terms of communication costs and robustness to RATF estimation errors and TAD errors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1434–1448},
numpages = {15}
}

@article{10.1109/TASLP.2018.2819941,
author = {Yu, Kai and Zhao, Zijian and Wu, Xueyang and Lin, Hongtao and Liu, Xuan},
title = {Rich Short Text Conversation Using Semantic-Key-Controlled Sequence Generation},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2819941},
doi = {10.1109/TASLP.2018.2819941},
abstract = {With the recent advances of the sequence-to-sequence framework, generation approaches for the short text conversation STC become attractive. The traditional sequence-to-sequence approaches for the STC often suffer from poor diversity and general reply without substantiality. It is also hard to control the topic or semantics of the selected reply from multiple generated candidates. In this paper, a novel external-memory-driven sequence-to-sequence learning approach is proposed to address these problems. A tensor of the external memory is constructed to represent interpretable topics or semantics. During generation, a controllable memory trigger is extracted given the input sequence, and a reply is then generated using the memory trigger as well as the sequence-to-sequence model. Experiments show that the proposed approach can generate much richer diversity than the traditional sequence-to-sequence training with attention. Meanwhile, it achieves better quality score in human evaluation. It is also observed that by manually manipulating the memory trigger, it is possible to interpretably guide the topics or semantics of the reply.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1359–1368},
numpages = {10}
}

@article{10.1109/TASLP.2018.2825108,
author = {Lehner, Bernhard and Schluter, Jan and Widmer, Gerhard},
title = {Online, Loudness-Invariant Vocal Detection in Mixed Music Signals},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2825108},
doi = {10.1109/TASLP.2018.2825108},
abstract = {Singing voice detection, also referred to as vocal detection VD, aims at automatically identifying the regions in a music recording where at least one person sings. It is highly challenging due to the timbral and expressive richness of the human singing voice, as well as the practically endless variety of interfering instrumental accompaniment. Additionally, certain instruments have an inherent risk of being misclassified as vocals due to similarities of the sound production system. In this paper, we present a machine learning approach that is based on our previous work for VD, which is specifically designed to deal with those challenging conditions. The contribution of this paper is threefold: First, we present a new method for VD that passes a compact set of features to a long short-term memory recurrent neural network classifier that obtains state-of-the-art results. Second, we thoroughly evaluate the proposed method along with related approaches to really probe the weaknesses of the methods. In order to allow for such a thorough evaluation, we make a curated collection of datasets available to the research community. Finally, we focus on a specific problem that was not obvious and had not been discussed in the literature so far. The reason for this is precisely because limited evaluations had not revealed this as a problem: the lack of loudness invariance. We will discuss the implications of utilizing loudness-related features and show that our method successfully deals with this problem due to the specific set of features it uses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1369–1380},
numpages = {12}
}

@article{10.1109/TASLP.2018.2825432,
author = {Tan, Tian and Qian, Yanmin and Hu and Zhou, Ying and Ding, Wen and Yu, Kai},
title = {Adaptive Very Deep Convolutional Residual Network for Noise Robust Speech Recognition},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2825432},
doi = {10.1109/TASLP.2018.2825432},
abstract = {Although great progress has been made in automatic speech recognition, significant performance degradation still exists in noisy environments. Our previous work has demonstrated the superior noise robustness of very deep convolutional neural networks VDCNN. Based on our work on VDCNNs, this paper proposes a more advanced model referred to as the very deep convolutional residual network VDCRN. This new model incorporates batch normalization and residual learning, showing more robustness than previous VDCNNs.Then, to alleviate the mismatch between the training and testing conditions, model adaptation and adaptive training are developed and compared for the new VDCRN. This paper focuses on factor aware training FAT and cluster adaptive training CAT. For FAT, a unified framework is explored. For CAT, two schemes are first explored to construct the bases in the canonical model; furthermore, a factorized version of CAT is designed to address multiple nonspeech variabilities in one model. Finally, a complete multipass system is proposed to achieve the best system performance in the noisy scenarios. The proposed new approaches are evaluated on three different tasks: Aurora4 simulated data with additive noise and channel distortion, CHiME4 both simulated and real data with additive noise and reverberation, and the AMI meeting transcription task real data with significant reverberation.The evaluation not only includes different noisy conditions, but also covers both simulated and real noisy data. The experiments show that the new VDCRN is more robust, and the adaptation on this model can further significantly reduce the word error rate WER. The proposed best architecture obtains consistent and very large improvements on all tasks compared to the baseline VDCNN or long short-term memory. Particularly, on Aurora4 a new milestone 5.67% WER is achieved by only improving acoustic modeling.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1393–1405},
numpages = {13}
}

@article{10.1109/TASLP.2017.2788181,
author = {Yang, Liner and Zhang, Meishan and Liu, Yang and Sun, Maosong and Yu, Nan and Fu, Guohong},
title = {Joint POS Tagging and Dependence Parsing With Transition-Based Neural Networks},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2788181},
doi = {10.1109/TASLP.2017.2788181},
abstract = {While part-of-speech POS tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems. In this paper, we propose an approach to joint POS tagging and dependency parsing using transition-based neural networks. Three neural network based classifiers are designed to resolve shift/reduce, tagging, and labeling conflicts. Experiments show that our approach significantly outperforms previous methods for joint POS tagging and dependency parsing across a variety of natural languages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1352–1358},
numpages = {7}
}

@article{10.1109/TASLP.2017.2738443,
author = {Wang, Chien-Yao and Wang, Jia-Ching and Santoso, Andri and Chiang, Chin-Chin and Wu, Chung-Hsien},
title = {Sound Event Recognition Using Auditory-Receptive-Field Binary Pattern and Hierarchical-Diving Deep Belief Network},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2738443},
doi = {10.1109/TASLP.2017.2738443},
abstract = {Automatic sound event recognition SER has recently attracted renewed interest. Although practical SER system has many useful applications in everyday life, SER is challenging owing to the variations among sounds and noises in the real-world environment. This paper presents a novel feature extraction and classification method to solve the problem of SER. An audio–visual descriptor, called the auditory-receptive-field binary pattern, is designed based on the spectrogram image feature, the cepstral features, and the human auditory receptive field model. The extracted features are then fed into a classifier to perform event classification. The proposed classifier, called the hierarchical-diving deep belief network, is a deep neural network system that hierarchically learns the discriminative characteristics from physical feature representation to the abstract concept. The performance of our proposed system was verified using several experiments under various conditions. Using the RWCP dataset, the proposed system achieved a recognition rate of 99.27% for real-world sound data in 105 categories. Under noisy conditions, the developed system is very robust, with which it achieved 95.06% recognition rate with 0 dB signal-to-noise ratio. Using the TUT sound event dataset, the proposed system achieves error rates of 0.81 and 0.73 in sound event detection in home and residential area scenes. The experimental results reveal that the proposed system outperformed the other systems in this field.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1336–1351},
numpages = {16}
}

@article{10.1109/TASLP.2018.2825601,
author = {Stone, Simon and Marxen, Michael and Birkholz, Peter},
title = {Construction and Evaluation of a Parametric One-Dimensional Vocal Tract Model},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2825601},
doi = {10.1109/TASLP.2018.2825601},
abstract = {Articulatory speech synthesis based on aero-acoustic simulations of the vocal tract is computationally expensive and, therefore, requires simple yet precise models. Modeling the one-dimensional vocal tract area function directly instead of a higher dimensional vocal tract model is an efficient way to minimize the computational overhead of the simulations. In this paper, we propose a new parametric vocal tract model that is controlled by six points and capable of modeling a large variety of vocal tract shapes. We geometrically and perceptually evaluated the model on a set of 22 reference area functions corresponding to German vowels and consonants. The model was able to geometrically approximate the reference area functions with a minimum root-mean-square error of 0.302 cm$^2$, a maximum error of 1.142 cm$^2$, and a median error of 0.891 cm$^2$. After optimizations, a perceptual evaluation of the synthesis using our model in combination with a state-of-the-art aero-acoustic simulation achieved a vowel recognition rate of 90.7% and a consonant recognition rate of 73.2%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1381–1392},
numpages = {12}
}

@article{10.1109/TASLP.2018.2825440,
author = {Rafii, Zafar and Liutkus, Antoine and Stoter, Fabian-Robert and Mimilakis, Stylianos Ioannis and FitzGerald, Derry and Pardo, Bryan},
title = {An Overview of Lead and Accompaniment Separation in Music},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2825440},
doi = {10.1109/TASLP.2018.2825440},
abstract = {Popular music is often composed of an accompaniment and a lead component, the latter typically consisting of vocals. Filtering such mixtures to extract one or both components has many applications, such as automatic karaoke and remixing. This particular case of source separation yields very specific challenges and opportunities, including the particular complexity of musical structures, but also relevant prior knowledge coming from acoustics, musicology or sound engineering. Due to both its importance in applications and its challenging difficulty, lead and accompaniment separation has been a popular topic in signal processing for decades. In this article, we provide a comprehensive review of this research topic, organizing the different approaches according to whether they are model-based or data-centered. For model-based methods, we organize them according to whether they concentrate on the lead signal, the accompaniment, or both. For data-centered approaches, we discuss the particular difficulty of obtaining data for learning lead separation systems, and then review recent approaches, notably those based on deep learning. Finally, we discuss the delicate problem of evaluating the quality of music separation through adequate metrics and present the results of the largest evaluation, to-date, of lead and accompaniment separation systems. In conjunction with the above, a comprehensive list of references is provided, along with relevant pointers to available implementations and repositories.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1307–1335},
numpages = {29}
}

@article{10.1109/TASLP.2018.2828650,
author = {Wang, Xin and Takaki, Shinji and Yamagishi, Junichi},
title = {Autoregressive Neural F0 Model for Statistical Parametric Speech Synthesis},
year = {2018},
issue_date = {August 2018},
publisher = {IEEE Press},
volume = {26},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2828650},
doi = {10.1109/TASLP.2018.2828650},
abstract = {Recurrent neural networks RNNs have been successfully used as fundamental frequency F0 models for text-to-speech synthesis. However, this paper showed that a normal RNN may not take into account the statistical dependency of the F0 data across frames and consequently only generate noisy F0 contours when F0 values are sampled from the model. A better model may take into account the causal dependency of the current F0 datum on the previous frames’ F0 data. One such model is the shallow autoregressive AR recurrent mixture density network SAR that we recently proposed. However, as this study showed, an SAR is equivalent to the combination of trainable linear filters and a conventional RNN. It is still weak for F0 modeling. To better model the temporal dependency in F0 contours, we propose a deep AR model DAR. On the basis of an RNN, this DAR propagates the previous frame's F0 value through the RNN, which allows nonlinear AR dependency to be achieved. We also propose F0 quantization and data dropout strategies for the DAR. Experiments on a Japanese corpus demonstrated that this DAR can generate appropriate F0 contours by using the random-sampling-based generation method, which is impossible for the baseline RNN and SAR. When a conventional mean-based generation method was used in the proposed DAR and other experimental models, the DAR generated accurate and less oversmoothed F0 contours and achieved a better mean-opinion-score in a subjective evaluation test.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1406–1419},
numpages = {14}
}

@article{10.1109/TASLP.2018.2821899,
author = {Santos, Joao Felipe and Falk, Tiago H.},
title = {Speech Dereverberation With Context-Aware Recurrent Neural Networks},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2821899},
doi = {10.1109/TASLP.2018.2821899},
abstract = {In this paper, we propose a model to perform speech dereverberation by estimating its spectral magnitude from the reverberant counterpart. Our models are capable of extracting features that take into account both short- and long-term dependencies in the signal through a convolutional encoder which extracts features from a short, bounded context of frames and a recurrent neural network for extracting long-term information. Our model outperforms a recently proposed model that uses different context information depending on the reverberation time, without requiring any sort of additional input, yielding improvements of up to 0.4 on perceptual evaluation of speech quality, 0.3 on short-time objective intelligibility, and 1.0 on perceptual objective listening quality assessment relative to reverberant speech. We also show our model is able to generalize to real room impulse responses even when only trained with simulated room impulse responses, different speakers, and high reverberation times. Finally, listening tests show the proposed method outperforming benchmark models in reduction of perceived reverberation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1232–1242},
numpages = {11}
}

@article{10.1109/TASLP.2018.2825110,
author = {Farmani, Mojtaba and Pedersen, Michael Syskind and Tan, Zheng-Hua and Jensen, Jesper},
title = {Bias-Compensated Informed Sound Source Localization Using Relative Transfer Functions},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2825110},
doi = {10.1109/TASLP.2018.2825110},
abstract = {In this paper, we consider the problem of estimating the target sound direction of arrival DoA for a hearing aid HA system, which can connect to a wireless microphone worn by the talker of interest. The wireless microphone “informs” the HA system about the noise-free target speech. To estimate the DoA, we consider a maximum-likelihood approach, and we assume that a database of DoA-dependent relative transfer functions RTFs has been measured in advance and is available. The proposed DoA estimator is able to take the available noise-free target speech, ambient noise characteristics, and the shadowing effect of the user's head on the received signals into account, and it supports both monaural and binaural microphone array configurations. Moreover, we analytically analyze the bias in the proposed estimator and introduce a modified estimator, which has been compensated for the bias. We demonstrate that the proposed method has lower computational complexity and better performance than recent RTF-based estimators. Furthermore, to decrease the number of parameters required to be wirelessly exchanged between the HAs in binaural configurations, we propose an information fusion strategy, which avoids transmitting microphone signals between the HAs. An important benefit of the proposed IF strategy is that the number of parameters to be exchanged between the HAs is independent of the number of HA microphones. Finally, we investigate the performance of variants of the proposed estimator extensively in different noisy and reverberant situations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1271–1285},
numpages = {15}
}

@article{10.1109/TASLP.2018.2819900,
author = {Del-Agua, Miguel Angel and Gimenez, Adria and Sanchis, Albert and Civera, Jorge and Juan, Alfons},
title = {Speaker-Adapted Confidence Measures for ASR Using Deep Bidirectional Recurrent Neural Networks},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2819900},
doi = {10.1109/TASLP.2018.2819900},
abstract = {In the last years, deep bidirectional recurrent neural networks DBRNN and DBRNN with long short-term memory cells DBLSTM have outperformed the most accurate classifiers for confidence estimation in automatic speech recognition. At the same time, we have recently shown that speaker adaptation of confidence measures using DBLSTM yields significant improvements over non-adapted confidence measures. In accordance with these two recent contributions to the state of the art in confidence estimation, this paper presents a comprehensive study of speaker-adapted confidence measures using DBRNN and DBLSTM models. First, we present new empirical evidences of the superiority of recurrent neural networks RNN-based confidence classifiers evaluated over a large speech corpus consisting of the English LibriSpeech and the Spanish poliMedia tasks. Second, we show new results on speaker-adapted confidence measures considering a multitask framework in which RNN-based confidence classifiers trained with LibriSpeech are adapted to speakers of the TED-LIUM corpus. These experiments confirm that speaker-adapted confidence measures outperform their non-adapted counterparts. Last, we describe an unsupervised adaptation method of the acoustic DBLSTM model based on confidence measures that results in better automatic speech recognition performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1194–1202},
numpages = {9}
}

@article{10.1109/TASLP.2018.2823081,
author = {Marquardt, Daniel and Doclo, Simon},
title = {Interaural Coherence Preservation for Binaural Noise Reduction Using Partial Noise Estimation and Spectral Postfiltering},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2823081},
doi = {10.1109/TASLP.2018.2823081},
abstract = {The objective of binaural speech enhancement algorithms is to reduce the undesired noise component, while preserving the desired speech source and the binaural cues of all sound sources. For the scenario of a single desired speech source in a diffuse noise field, an extension of the binaural multichannel Wiener filter MWF, namely the MWF-IC, has been recently proposed, which aims to preserve the interaural coherence IC of the noise component. However, due to the large complexity of the MWF-IC, in this paper we propose several alternative algorithms at a lower computational complexity. First, we consider a quasi-distortionless version of the MWF-IC, denoted as minimum-variance-distortionless response MVDR-IC. Second, we propose to preserve the IC of the noise component using the binaural MWF with partial noise estimation MWF-N and the binaural MVDR beamformer with partial noise estimation MVDR-N, for which closed-form expressions exist. In addition, we show that for the MVDR-N a closed-form expression can be derived for the tradeoff parameter yielding a desired magnitude squared coherence MSC for the output noise component. Since contrary to the MWF-IC and the MWF-N the MVDR-IC and the MVDR-N do not take into account the spectro-temporal properties of the speech and the noise components, we propose to apply a spectral postfilter to the filter outputs, improving the noise reduction performance. The performance of all algorithms is compared in several diffuse noise scenarios. The simulation results show that both the MVDR-IC and the MVDR-N are able to preserve the MSC of the noise component, while generally the MVDR-IC shows a slightly better noise reduction performance at a larger complexity. Further, simulation results show that applying a spectral postfilter leads to a very similar performance for all considered algorithms in terms of noise reduction and speech distortion.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1257–1270},
numpages = {14}
}

@article{10.1109/TASLP.2018.2815268,
author = {Tao, Fei and Busso, Carlos},
title = {Gating Neural Network for Large Vocabulary Audiovisual Speech Recognition},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2815268},
doi = {10.1109/TASLP.2018.2815268},
abstract = {Audio-based automatic speech recognition A-ASR systems are affected by noisy conditions in real-world applications. Adding visual cues to the ASR system is an appealing alternative to improve the robustness of the system, replicating the audiovisual perception process used during human interactions. A common problem observed when using audiovisual automatic speech recognition AV-ASR is the drop in performance when speech is clean. In this case, visual features may not provide complementary information, introducing variability that negatively affects the performance of the system. The experimental evaluation in this study clearly demonstrates this problem when we train an audiovisual state-of-the-art hybrid system with a deep neural network DNN and hidden Markov models HMMs. This study proposes a framework that addresses this problem, improving, or at least, maintaining the performance when visual features are used. The proposed approach is a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual features, keeping only useful information. The framework is implemented with a subset of the audiovisual CRSS-4ENGLISH-14 corpus which consists of 61 h of speech from 105 subjects simultaneously collected with multiple cameras and microphones. The proposed framework is compared with conventional HMMs with observation models implemented with either a Gaussian mixture model or DNNs. We also compare the system with a multi-stream HMM system. The experimental evaluation indicates that the proposed framework outperforms alternative methods under all configurations, showing the robustness of the gating-based framework for AV-ASR.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1286–1298},
numpages = {13}
}

@article{10.1109/TASLP.2018.2818408,
author = {Yoshimura, Takenori and Hashimoto, Kei and Oura, Keiichiro and Nankaku, Yoshihiko and Tokuda, Keiichi},
title = {Mel-Cepstrum-Based Quantization Noise Shaping Applied to Neural-Network-Based Speech Waveform Synthesis},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2818408},
doi = {10.1109/TASLP.2018.2818408},
abstract = {This paper presents a mel-cepstrum-based quantization noise shaping method for improving the quality of synthetic speech generated by neural-network-based speech waveform synthesis systems. Since mel-cepstral coefficients closely match the characteristics of human auditory perception, the proposed method effectively masks the white noise introduced by the quantization typically used in neural-network-based speech waveform synthesis systems. The paper also describes a computationally efficient implementation of the proposed method using the structure of the mel-log spectrum approximation filter. Experiments using the WaveNet generative model, which is a state-of-the-art model for neural-network-based speech waveform synthesis, showed that speech quality is significantly improved by the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1173–1180},
numpages = {8}
}

@article{10.1109/TASLP.2018.2817798,
author = {Wang, Qing and Du, Jun and Dai, Li-Rong and Lee, Chin-Hui},
title = {A Multiobjective Learning and Ensembling Approach to High-Performance Speech Enhancement With Compact Neural Network Architectures},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2817798},
doi = {10.1109/TASLP.2018.2817798},
abstract = {In this study, we propose a novel deep neural network DNN architecture for speech enhancement SE via a multiobjective learning and ensembling MOLE framework to achieve a compact and lowlatency design, while maintaining good performance in quality evaluations. MOLE follows the boosting concept when combining weak models into a strong classifier and consists of two compact DNNs. The first, called the multiobjective learning DNN MOL-DNN, takes multiple features, such as log-power spectra LPS, mel-frequency cepstral coefficients MFCCs and Gammatone frequency cepstral coefficients GFCCs to predict a multiobjective set that includes clean speech feature, dynamic noise feature, and ideal ratio mask IRM. The second, called the multiobjective ensembling DNN MOE-DNN, takes the learned features from MOL-DNN as inputs and separately predicts clean LPS and IRM, clean MFCC and IRM, and clean GFCC and IRM using three sets of weak regression functions. Finally, a postprocessing operation can be applied to the estimated clean features by leveraging the multiple targets learned from both the MOL-DNN and the MOE-DNN. On speech corrupted by 15 noise types not seen in model training the SE results show that the MOLE approach, which features a small model size and low run-time latency, can achieve consistent improvements over both DNN- and long short-term memory LSTM-based techniques in terms of all the objective metrics evaluated in this study for all three cases the input contexts contain 1-frame, 4-frame and 7-frame instances. The 1-frame MOLE-based SE system outperforms the DNN-based SE system with a 7-frame input expansion at a 3-frame delay and also achieves better performance than the LSTM-based SE system with 4-frame, no delay expansion by including only 3 previous frames, and with 170 times less processing latency.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1181–1193},
numpages = {13}
}

@article{10.1109/TASLP.2018.2819819,
author = {Stankovic, Ljubisa and Brajovic, Milos},
title = {Analysis of the Reconstruction of Sparse Signals in the DCT Domain Applied to Audio Signals},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2819819},
doi = {10.1109/TASLP.2018.2819819},
abstract = {Sparse signals can be reconstructed from a reduced set of signal samples using compressive sensing CS methods. The discrete cosine transform DCT can provide highly concentrated representations of audio signals. This property implies the DCT as a good sparsity domain for the audio signals. In this paper, the DCT is studied within the context of sparse audio signal processing using the CS theory and methods. The DCT coefficients of a sparse signal, calculated with a reduced set of available samples, can be modeled as random variables. It has been shown that the statistical properties of these variables are closely related to the unique reconstruction conditions. The main result of this paper is in an exact formula for the mean-square reconstruction error in the case of approximately sparse and nonsparse noisy signals reconstructed under the sparsity assumption. Based on the presented analysis, a simple and computationally efficient reconstruction algorithm is proposed. The presented theoretical concepts and the efficiency of the reconstruction algorithm are verified numerically, including examples with synthetic and recorded audio signals with unavailable or corrupted samples. Random disturbances and disturbances simulating clicks or inpainting in audio signals are considered. Statistical verification is done on a dataset with experimental signals. Results are compared with some classical and recent methods used in similar signal and disturbance scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1216–1231},
numpages = {16}
}

@article{10.1109/TASLP.2018.2821846,
author = {Geronazzo, Michele and Spagnol, Simone and Avanzini, Federico},
title = {Do We Need Individual Head-Related Transfer Functions for Vertical Localization? The Case Study of a Spectral Notch Distance Metric},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2821846},
doi = {10.1109/TASLP.2018.2821846},
abstract = {This paper deals with the issue of individualizing the head-related transfer function HRTF rendering process for auditory elevation perception. Is it possible to find a nonindividual, personalized HRTF set that allows a listener to have an equally accurate localization performance than with his/her individual HRTFs? We propose a psychoacoustically motivated, anthropometry based mismatch function between HRTF pairs that exploits the close relation between the listener's pinna geometry and localization cues. This is evaluated using an auditory model that computes a mapping between HRTF spectra and perceived spatial locations. Results on a large number of subjects in the center for image processing and integrated computing CIPIC and acoustics research institute ARI HRTF databases suggest that there exists a nonindividual HRTF set, which allows a listener to have an equally accurate vertical localization than with individual HRTFs. Furthermore, we find the optimal parameterization of the proposed mismatch function, i.e., the one that best reflects the information given by the auditory model. Our findings show that the selection procedure yields statistically significant improvements with respect to dummy-head HRTFs or random HRTF selection, with potentially high impact from an applicative point of view.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1243–1256},
numpages = {14}
}

@article{10.1109/TASLP.2018.2820429,
author = {Proenca, Jorge and Lopes, Carla and Tjalve, Michael and Stolcke, Andreas and Candeias, Sara and Perdigao, Fernando},
title = {Mispronunciation Detection in Children's Reading of Sentences},
year = {2018},
issue_date = {July 2018},
publisher = {IEEE Press},
volume = {26},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2820429},
doi = {10.1109/TASLP.2018.2820429},
abstract = {This paper proposes an approach to automatically parse children's reading of sentences by detecting word pronunciations and extra content, and to classify words as correctly or incorrectly pronounced. This approach can be directly helpful for automatic assessment of reading level or for automatic reading tutors, where a correct reading must be identified. We propose a first segmentation stage to locate candidate word pronunciations based on allowing repetitions and false starts of a word's syllables. A decoding grammar based solely on syllables allows silence to appear during a word pronunciation. At a second stage, word candidates are classified as mispronounced or not. The feature that best classifies mispronunciations is found to be the log-likelihood ratio between a free phone loop and a word spotting model in the very close vicinity of the candidate segmentation. Additional features are combined in multifeature models to further improve classification, including: normalizations of the log-likelihood ratio, derivations from phone likelihoods, and Levenshtein distances between the correct pronunciation and recognized phonemes through two phoneme recognition approaches. Results show that most extra events were detected close to 2% word error rate achieved and that using automatic segmentation for mispronunciation classification approaches the performance of manual segmentation. Although the log-likelihood ratio from a spotting approach is already a good metric to classify word pronunciations, the combination of additional features provides a relative reduction of the miss rate of 18% from 34.03% to 27.79% using manual segmentation and from 35.58% to 29.35% using automatic segmentation, at constant 5% false alarm rate.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1203–1215},
numpages = {13}
}

@article{10.1109/TASLP.2018.2804172,
author = {Braun, Sebastian and Kuklasinski, Adam and Schwartz, Ofer and Thiergart, Oliver and Habets, Emanuel A. P. and Gannot, Sharon and Doclo, Simon and Jensen, Jesper},
title = {Evaluation and Comparison of Late Reverberation Power Spectral Density Estimators},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2804172},
doi = {10.1109/TASLP.2018.2804172},
abstract = {Reduction of late reverberation can be achieved using spatio-spectral filters, such as the multichannel Wiener filter. To compute this filter, an estimate of the late reverberation power spectral density PSD is required. In recent years, a multitude of late reverberation PSD estimators have been proposed. In this paper, these estimators are categorized into several classes, their relations and differences are discussed, and a comprehensive experimental comparison is provided. To compare their performance, simulations in controlled as well as practical scenarios are conducted. It is shown that a common weakness of spatial coherence-based estimators is their performance in high direct-to-diffuse ratio conditions. To mitigate this problem, a correction method is proposed and evaluated. It is shown that the proposed correction method can decrease the speech distortion without significantly affecting the reverberation reduction.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1052–1067},
numpages = {16}
}

@article{10.1109/TASLP.2018.2798804,
author = {Donley, Jacob and Ritz, Christian and Kleijn, W. Bastiaan},
title = {Multizone Soundfield Reproduction With Privacy- and Quality-Based Speech Masking Filters},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2798804},
doi = {10.1109/TASLP.2018.2798804},
abstract = {Reproducing zones of personal sound is a challenging signal processing problem that has garnered considerable research interest in recent years. We introduce in this work an extended method to multizone soundfield reproduction that overcomes issues with speech privacy and quality. Measures of speech intelligibility contrast SIC and speech quality are used as cost functions in an optimization of speech privacy and quality. Novel spatial and temporal frequency domain speech masker filter designs are proposed to accompany the optimization process. Spatial masking filters are designed using multizone soundfield algorithms that are dependent on the target speech multizone reproduction. Combinations of estimates of acoustic contrast and long term average speech spectra are proposed to provide equal masking influence on speech privacy and quality. Spatial aliasing specific to multizone soundfield reproduction geometry is further considered in analytically derived low-pass filters. Simulated and real-world experiments are conducted to verify the performance of the proposed method using semi-circular and linear loudspeaker arrays. Simulated implementations of the proposed method show that significant SIC and speech quality is achievable between zones. A range of perceptual evaluation of speech quality mean opinion scores that indicate good quality are obtained while at the same time providing confidential privacy as indicated by SIC. The simulations also show that the method is robust to variations in the speech, virtual source location, array geometry, and number of loudspeakers. Real-world experiments confirm the practicality of the proposed methods by showing that good quality and confidential privacy are achievable.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1037–1051},
numpages = {15}
}

@article{10.1109/TASLP.2018.2795746,
author = {Kameoka, Hirokazu and Higuchi, Takuya and Tanaka, Mikihiro and Li},
title = {Nonnegative Matrix Factorization With Basis Clustering Using Cepstral Distance Regularization},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2795746},
doi = {10.1109/TASLP.2018.2795746},
abstract = {One successful approach for audio source separation involves applying nonnegative matrix factorization NMF to a magnitude spectrogram regarded as a nonnegative matrix. This can be interpreted as approximating the observed spectra at each time frame as the linear sum of the basis spectra scaled by time-varying amplitudes. This paper deals with the problem of the unsupervised instrument-wise source separation of polyphonic signals based on an extension of the NMF approach. We focus on the fact that each piece of music is typically played on a handful of musical instruments, which allows us to assume that the spectra of the underlying audio events in a polyphonic signal can be grouped into a reasonably small number of clusters in the mel-frequency cepstral coefficient MFCC domain. Based on this assumption, we propose formulating factorization of a magnitude spectrogram and clustering of the basis spectra in the MFCC domain as a joint optimization problem and derive a novel optimization algorithm based on the majorization–minimization principle. Experimental results revealed that our method was superior to a two-stage algorithm that consists of performing factorization followed by clustering the basis spectra, thus showing the advantage of the joint optimization approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1025–1036},
numpages = {12}
}

@article{10.1109/TASLP.2018.2816241,
author = {Krawczyk-Becker, Martin and Gerkmann, Timo},
title = {On Speech Enhancement Under PSD Uncertainty},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2816241},
doi = {10.1109/TASLP.2018.2816241},
abstract = {Many well-known and frequently employed Bayesian clean speech estimators have been derived under the assumption that the true power spectral densities PSDs of speech and noise are exactly known. In practice, however, only power spectral density PSD estimates are available. Simply neglecting PSD estimation errors and handling the estimates as true values leads to speech estimation errors causing musical noise and undesired suppression of speech. In this paper, the uncertainty of the available speech PSD estimates is addressed. The main contributions are the following. First, we summarize and examine ways to model and incorporate the uncertainty of PSD estimates for a more robust speech enhancement performance. Second, a novel nonlinear clean speech estimator is derived that takes into account prior knowledge about the absolute value of typical speech PSDs. Third, we show that the derived statistical framework provides uncertainty-aware counterparts to a number of well-known conventional clean speech estimators such as the Wiener filter and Ephraim and Malah's amplitude estimators. Fourth, we show how modern PSD estimators can be incorporated into the theoretical framework and propose to employ frequency dependent priors. Finally, the effects and benefits of considering the uncertainty of speech PSD estimates are analyzed, discussed, and evaluated via instrumental measures and a listening experiment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1140–1149},
numpages = {10}
}

@article{10.1109/TASLP.2018.2811247,
author = {Braun, Sebastian and Habets, Emanuel A. P.},
title = {Linear Prediction-Based Online Dereverberation and Noise Reduction Using Alternating Kalman Filters},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2811247},
doi = {10.1109/TASLP.2018.2811247},
abstract = {Multichannel linear prediction-based dereverberation in the short-time Fourier transform STFT domain has been shown to be highly effective. Using this framework, the desired dereverberated multichannel signal is obtained by filtering the noise-free reverberant signals using the estimated multichannel autoregressive MAR coefficients. To use such methods in the presence of noise, especially in the case of online processing, remains a challenging problem. Existing sequential enhancement structures, which first remove the noise and then estimate the MAR coefficients, suffer from a causality problem as both the optimal noise reduction and dereverberation stages depend on the current output of each other. To address this problem, an algorithm that consists of two alternating Kalman filters to estimate the noise-free reverberant signals and the MAR coefficients is proposed. The causality of the estimation procedure is important when dealing with time-variant acoustic scenarios, where the MAR coefficients are time-varying. The proposed method is evaluated using simulated and measured acoustic impulse responses and is compared to a method based on the same signal model. In addition, a method to control the reverberation reduction and noise reduction independently is derived.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1115–1125},
numpages = {11}
}

@article{10.1109/TASLP.2018.2811540,
author = {Magron, Paul and Badeau, Roland and David, Bertrand},
title = {Model-Based STFT Phase Recovery for Audio Source Separation},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2811540},
doi = {10.1109/TASLP.2018.2811540},
abstract = {For audio source separation applications, it is common to estimate the magnitude of the short-time Fourier transform STFT of each source. In order to further synthesize time-domain signals, it is necessary to recover the phase of the corresponding complex-valued STFT. Most authors in this field choose a Wiener-like filtering approach, which boils down to use the phase of the original mixture. In this paper, a different standpoint is adopted. Many music events are partially composed of slowly varying sinusoids and the STFT phase increment over time of those frequency components takes a specific form. This allows phase recovery by an unwrapping technique once a short-term frequency estimate has been obtained. Herein, a novel iterative source separation procedure is proposed that builds upon these results. It consists in minimizing the mixing error by means of the auxiliary function method. This procedure is initialized by exploiting the unwrapping technique in order to generate estimates that benefit from a temporal continuity property. Experiments conducted on realistic music pieces show that, given accurate magnitude estimates, this procedure outperforms the state-of-the-art consistent Wiener filter.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1091–1101},
numpages = {11}
}

@article{10.1109/TASLP.2018.2811184,
author = {Kodrasi, Ina and Doclo, Simon},
title = {Analysis of Eigenvalue Decomposition-Based Late Reverberation Power Spectral Density Estimation},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2811184},
doi = {10.1109/TASLP.2018.2811184},
abstract = {Many speech dereverberation techniques require an estimate of the late reverberation power spectral density PSD. State-of-the-art multichannel methods for estimating the late reverberation PSD typically rely on first, an estimate of the relative transfer functions RTFs of the target signal; second, a model for the spatial coherence matrix of the late reverberation; and finally, an estimate of the reverberant speech or reverberant and noisy speech PSD matrix. The RTFs, the spatial coherence matrix, and the speech PSD matrix are all prone to modeling and estimation errors in practice, with the RTFs being particularly difficult to estimate accurately, especially in highly reverberant and noisy scenarios. Recently, we proposed an eigenvalue decomposition EVD-based late reverberation PSD estimator, which does not require an estimate of the RTFs. In this paper, this EVD-based PSD estimator is further analyzed and its estimation accuracy and computational complexity are analytically compared to a state-of-the-art maximum likelihood ML based PSD estimator. It is shown that for perfect knowledge of the RTFs, spatial coherence matrix, and reverberant speech PSD matrix, the ML-based and the EVD-based PSD estimates are both equal to the true late reverberation PSD. In addition, it is shown that for erroneous RTFs but perfect knowledge of the spatial coherence matrix and reverberant speech PSD matrix, the ML-based PSD estimate is larger than or equal to the true late reverberation PSD, whereas the EVD-based PSD estimate is obviously still equal to the true late reverberation PSD. Finally, it is shown that when modeling and estimation errors occur in all quantities, the ML-based PSD estimate is larger than or equal to the EVD-based PSD estimate. Simulation results for several realistic acoustic scenarios demonstrate the advantages of using the EVD-based PSD estimator in a multichannel Wiener filter, yielding a significantly better performance than the ML-based PSD estimator.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1102–1114},
numpages = {13}
}

@article{10.1109/TASLP.2018.2815780,
author = {Ram, Dhananjay and Asaei, Afsaneh and Bourlard, Herve},
title = {Sparse Subspace Modeling for Query by Example Spoken Term Detection},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2815780},
doi = {10.1109/TASLP.2018.2815780},
abstract = {This paper focuses on the problem of query by example spoken term detection QbE-STD in zero-resource scenario. Current state-of-the-art approaches to tackle this problem rely on dynamic programming based template matching techniques using phone posterior features extracted at the output of a deep neural network. Previously, it has been shown that the space of phone posteriors is highly structured, as a union of low-dimensional subspaces. To exploit the temporal and sparse structure of the speech data, we investigate here three different QbE-STD systems based on sparse model recovery. More specifically, we use query examples to model the query subspace using dictionary for sparse coding. Reconstruction errors calculated using sparse representation of feature vectors are then used to characterize the underlying subspaces. The first approach uses these reconstruction errors in a dynamic programming framework to detect the spoken query, resulting in a much faster search compared to standard template matching. The other two methods aim at merging template matching and sparsity-based approaches to further improve the performance. The first one proposes to regularize the template matching local distances using sparse reconstruction errors. The second approach aims at using the sparse reconstruction errors to rescore improve the template matching likelihood. Experiments on two different databases AMI and MediaEval show that the proposed hybrid systems perform better than a highly competitive QbE-STD baseline system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun

license_type: permissive

branch_name: refs/heads/main

extension: bib