text:
quality aspects contribute stronger to overall audio quality. The results indicate that overall audio quality depends on the lower quality aspect, either monaural or binaural.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1112–1125},
numpages = {14}
}

@article{10.1109/TASLP.2019.2911164,
author = {Yusuf, Bolaji and Gundogdu, Batuhan and Saraclar, Murat},
title = {Low Resource Keyword Search With Synthesized Crosslingual Exemplars},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2911164},
doi = {10.1109/TASLP.2019.2911164},
abstract = {The transfer of acoustic data across languages has been shown to improve keyword search KWS performance in data-scarce settings. In this paper, we propose a way of performing this transfer that reduces the impact of the prevalence of out-of-vocabulary OOV terms on KWS in such a setting. We investigate a novel usage of multilingual features for KWS with very little training data in the target languages. The crux of our approach is the use of synthetic phone exemplars to convert the search into a query-by-example task, which we solve with the dynamic time warping algorithm. Using bottleneck features obtained from a network trained multilingually on a set of source languages, we train an extended distance metric learner EDML for four target languages from the IARPA Babel program which are distinct from the source languages. Compared with a baseline system that is based on automatic speech recognition ASR with a multilingual acoustic model, we observe an average term weighted value improvement of ${0.0603}$ absolute $text{74}%$ relative in a setting with only 1 h of training data in the target language. When the data scarcity is relaxed to 10 h, we find that phone posteriors obtained by fine-tuning the multilingual network give better EDML systems. In this relaxed setting, the EDML systems still perform better than the baseline on OOV terms. Given their complementary natures, combining the EDML and the ASR-based baseline results in even further performance improvements in all settings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1126–1135},
numpages = {10}
}

@article{10.1109/TASLP.2019.2912123,
author = {Cauchi, Benjamin and Siedenburg, Kai and Santos, Joao F. and Falk, Tiago H. and Doclo, Simon and Goetze, Stefan},
title = {Non-Intrusive Speech Quality Prediction Using Modulation Energies and LSTM-Network},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2912123},
doi = {10.1109/TASLP.2019.2912123},
abstract = {Many signal processing algorithms have been proposed to improve the quality of speech recorded in the presence of noise and reverberation. Perceptual measures, i.e., listening tests, are usually considered the most reliable way to evaluate the quality of speech processed by such algorithms but are costly and time-consuming. Consequently, speech enhancement algorithms are often evaluated using signal-based measures, which can be either intrusive or non-intrusive. As the computation of intrusive measures requires a reference signal, only non-intrusive measures can be used in applications for which the clean speech signal is not available. However, many existing non-intrusive measures correlate poorly with the perceived speech quality, particularly when applied over a wide range of algorithms or acoustic conditions. In this paper, we propose a novel non-intrusive measure of the quality of processed speech that combines modulation energy features and a recurrent neural network using long short-term memory cells. We collected a dataset of perceptually evaluated signals representing several acoustic conditions and algorithms and used this dataset to train and evaluate the proposed measure. Results show that the proposed measure yields higher correlation with perceptual speech quality than that of benchmark intrusive and non-intrusive measures when considering various categories of algorithms. Although the proposed measure is sensitive to mismatch between training and testing, results show that it is a useful approach to evaluate specific algorithms over a wide range of acoustic conditions and may, thus, become particularly useful for real-time selection of speech enhancement algorithm settings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1151–1163},
numpages = {13}
}

@article{10.1109/TASLP.2019.2913087,
author = {Zhang, Yike and Zhang, Pengyuan and Yan, Yonghong},
title = {Tailoring an Interpretable Neural Language Model},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2913087},
doi = {10.1109/TASLP.2019.2913087},
abstract = {Neural networks have shown great potential in language modeling. Currently, the dominant approach to language modeling is based on recurrent neural networks RNNs and convolutional neural networks CNNs. Nonetheless, it is not clear why RNNs and CNNs are suitable for the language modeling task since these neural models are lack of interpretability. The goal of this paper is to tailor an interpretable neural model as an alternative to RNNs and CNNs for the language modeling task. This paper proposes a unified framework for language modeling, which can partly interpret the rationales behind existing language models LMs. Based on the proposed framework, an interpretable neural language model INLM is proposed, including a tailored architectural structure and a tailored learning method for the language modeling task. The proposed INLM can be approximated as a parameterized auto-regressive moving average model and provides interpretability in two aspects: component interpretability and prediction interpretability. Experiments demonstrate that the proposed INLM outperforms some typical neural LMs on several language modeling datasets and on the switchboard speech recognition task. Further experiments also show that the proposed INLM is competitive with the state-of-the-art long short-term memory LMs on the Penn Treebank and WikiText-2 datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1164–1178},
numpages = {15}
}

@article{10.1109/TASLP.2019.2913094,
author = {Luo, Huaishao and Li, Tianrui and Liu, Bing and Wang, Bin and Unger, Herwig},
title = {Improving Aspect Term Extraction With Bidirectional Dependency Tree Representation},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2913094},
doi = {10.1109/TASLP.2019.2913094},
abstract = {Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that using dependency tree structure representation is promising for this task. However, most dependency tree structures involve only one directional propagation on the dependency tree. In this paper, we first propose a novel bidirectional dependency tree network to extract dependency structure features from the given sentences. The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree. An end-to-end framework is then developed to integrate the embedded representations and BiLSTM plus CRF to learn both tree-structured and sequential features to solve the aspect term extraction problem. Experimental results demonstrate that the proposed model outperforms state-of-the-art baseline models on four benchmark SemEval datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1201–1212},
numpages = {12}
}

@article{10.1109/TASLP.2019.2913512,
author = {Pandey, Ashutosh and Wang, DeLiang},
title = {A New Framework for CNN-Based Speech Enhancement in the Time Domain},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2913512},
doi = {10.1109/TASLP.2019.2913512},
abstract = {This paper proposes a new learning mechanism for a fully convolutional neural network CNN to address speech enhancement in the time domain. The CNN takes as input the time frames of noisy utterance and outputs the time frames of the enhanced utterance. At the training time, we add an extra operation that converts the time domain to the frequency domain. This conversion corresponds to simple matrix multiplication, and is hence differentiable implying that a frequency domain loss can be used for training in the time domain. We use mean absolute error loss between the enhanced short-time Fourier transform STFT magnitude and the clean STFT magnitude to train the CNN. This way, the model can exploit the domain knowledge of converting a signal to the frequency domain for analysis. Moreover, this approach avoids the well-known invalid STFT problem since the proposed CNN operates in the time domain. Experimental results demonstrate that the proposed method substantially outperforms the other methods of speech enhancement. The proposed method is easy to implement and applicable to related speech processing tasks that require time-frequency masking or spectral mapping.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1179–1188},
numpages = {10}
}

@article{10.1109/TASLP.2019.2913089,
author = {Vikram, C. M. and Adiga, Nagaraj and Prasanna, S. R. Mahadeva},
title = {Detection of Nasalized Voiced Stops in Cleft Palate Speech Using Epoch-Synchronous Features},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2913089},
doi = {10.1109/TASLP.2019.2913089},
abstract = {The presence of velopharyngeal dysfunction in individuals with cleft palate CP nasalizes the voiced stops. Due to this, voiced stops /b/, /d/, /g/ tend to be perceive like nasal consonants /m/, /n/, /ng/. In this work, a novel algorithm is proposed for the detection of nasalized voiced stops in CP speech using epoch-synchronous features. Speech regions corresponding to consonant and consonant-vowel transitions are segmented using the knowledge of glottal activity, syllable nucleus, low-frequency spectral dominance, and vowel onset point. The segmented regions are epoch-synchronously processed to analyze the spectral, spectro-temporal, excitation source, and periodicity characteristics of normal and nasalized voiced stops. Spectral and spectro temporal features are computed using single pole filter based time-frequency representation. The amplitude of Hilbert envelope of linear prediction residual, measured around the epoch is used to analyze the effect of nasalization on excitation source. Comparison of speech frames of successive inter-epoch intervals is carried out to analyze the periodicity characteristics. The proposed features are used to develop a support vector machine classifier for the classification of normal and nasalized voiced stops. Segmentation accuracy for the proposed knowledge based method is found to be better than the hidden Markov model based force-alignment approach. The detection rate of nasalized voiced stops is found to be high for the proposed epoch synchronous features than the conventional Mel-frequency cepstral coefficients.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1189–1200},
numpages = {12}
}

@article{10.1109/TASLP.2019.2910637,
author = {Sisman, Berrak and Zhang, Mingyang and Li, Haizhou},
title = {Group Sparse Representation With WaveNet Vocoder Adaptation for Spectrum and Prosody Conversion},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2910637},
doi = {10.1109/TASLP.2019.2910637},
abstract = {The statistical approach to voice conversion typically consists of a feature conversion module followed by a vocoder. So far, the feature conversion studies are mainly focused on the conversion of spectrum. However, speaker identity is also characterized by prosodic features, such as fundamental frequency F0 and energy contour among others. In this paper, we study the transformation of speaker characteristics both in terms of spectrum and prosody. We propose two novel techniques that effectively use a limited amount of source-target training data and leverage a large general speech corpus to improve the voice conversion quality. First, we study the phonetic sparse representation under the group sparsity mathematical formulation. We use phonetic posteriorgrams PPGs together with spectral and prosody features to form tandem feature in the phonetic dictionary. The tandem feature allow us to estimate an activation matrix that is less dependent on source speakers, thus providing a better voice conversion quality. Second, we study the use of WaveNet vocoder that can be trained on general speech corpus from multiple speakers and adapted on target speaker data to improve the vocoding quality. We benefit from the large general speech databases that are used to train the PPG generator, and the WaveNet vocoder. The experiments show that the proposed conversion framework outperforms the traditional spectrum and prosody conversion techniques in both objective and subjective evaluations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1085–1097},
numpages = {13}
}

@article{10.1109/TASLP.2019.2906484,
author = {Juvela, Lauri and Bollepalli, Bajibabu and Tsiaras, Vassilis and Alku, Paavo},
title = {GlotNet—A Raw Waveform Model for the Glottal Excitation in Statistical Parametric Speech Synthesis},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2906484},
doi = {10.1109/TASLP.2019.2906484},
abstract = {Recently, generative neural network models which operate directly on raw audio, such as WaveNet, have improved the state of the art in text-to-speech synthesis TTS. Moreover, there is increasing interest in using these models as statistical vocoders for generating speech waveforms from various acoustic features. However, there is also a need to reduce the model complexity, without compromising the synthesis quality. Previously, glottal pulseforms i.e., time-domain waveforms corresponding to the source of human voice production mechanism have been successfully synthesized in TTS by glottal vocoders using straightforward deep feedforward neural networks. Therefore, it is natural to extend the glottal waveform modeling domain to use the more powerful WaveNet-like architecture. Furthermore, due to their inherent simplicity, glottal excitation waveforms permit scaling down the waveform generator architecture. In this study, we present a raw waveform glottal excitation model, called GlotNet, and compare its performance with the corresponding direct speech waveform model, WaveNet, using equivalent architectures. The models are evaluated as part of a statistical parametric TTS system. Listening test results show that both approaches are rated highly in voice similarity to the target speaker, and obtain similar quality ratings with large models. Furthermore, when the model size is reduced, the quality degradation is less severe for GlotNet.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1019–1030},
numpages = {12}
}

@article{10.1109/TASLP.2019.2910638,
author = {Lee, Jinkyu and Kang, Hong-Goo},
title = {A Joint Learning Algorithm for Complex-Valued T-F Masks in Deep Learning-Based Single-Channel Speech Enhancement Systems},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2910638},
doi = {10.1109/TASLP.2019.2910638},
abstract = {This paper presents a joint learning algorithm for complex-valued time-frequency T-F masks in single-channel speech enhancement systems. Most speech enhancement algorithms operating in a single-channel microphone environment aim to enhance the magnitude component in a T-F domain, while the input noisy phase component is used directly without any processing. Consequently, the mismatch between the processed magnitude and the unprocessed phase degrades the sound quality. To address this issue, a learning method of targeting a T-F mask that is defined in a complex domain has recently been proposed. However, due to a wide dynamic range and an irregular spectrogram pattern of the complex-valued T-F mask, the learning process is difficult even with a large-scale deep learning network. Moreover, the learning process targeting the T-F mask itself does not directly minimize the distortion in spectra or time domains. In order to address these concerns, we focus on three issues: 1 an effective estimation of complex numbers with a wide dynamic range; 2 a learning method that is directly related to speech enhancement performance; and 3 a way to resolve the mismatch between the estimated magnitude and phase spectra. In this study, we propose objective functions that can solve each of these issues and train the network by minimizing them with a joint learning framework. The evaluation results demonstrate that the proposed learning algorithm achieves significant performance improvement in various objective measures and subjective preference listening test.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1098–1109},
numpages = {12}
}

@article{10.1109/TASLP.2019.2905778,
author = {Liu, Yuanyuan and Lee, Tan and Law, Thomas and Lee, Kathy Yuet-Sheung},
title = {Acoustical Assessment of Voice Disorder With Continuous Speech Using ASR Posterior Features},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2905778},
doi = {10.1109/TASLP.2019.2905778},
abstract = {Traditionally acoustical assessment of voice disorder relies on simple and homogeneous speech samples like sustained vowels. Continuous speech is believed to be more representative of the daily function of voice and more preferable in clinical practice. This paper describes an attempt on automating voice assessment with continuous speech utterances. The proposed system makes use of a novel type of features that are derived from phone posterior probabilities outputted by a deep neural network based automatic speech recognition ASR system. These ASR-based voice features are designed to effectively quantify the mismatch between disordered voice and normal voice. Prediction of voice disorder severity is carried out first at utterance-level and subsequently the prediction scores for individual utterances from a subject are combined to give an overallassessment on the subject. With a low-dimension ASR-based feature vector, the utterance-level prediction accuracy is comparable to that with conventional features with a much higher dimension. By jointly using the ASR features and conventional voice features, a subject-level prediction accuracy of over $text{80}{%}$ on three severity classes can be achieved. Subjects with mild disorder and those with severe disorder could be perfectly distinguished by the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1047–1059},
numpages = {13}
}

@article{10.1109/TASLP.2019.2907016,
author = {Mesaros, Annamaria and Diment, Aleksandr and Elizalde, Benjamin and Heittola, Toni and Vincent, Emmanuel and Raj, Bhiksha and Virtanen, Tuomas},
title = {Sound Event Detection in the DCASE 2017 Challenge},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2907016},
doi = {10.1109/TASLP.2019.2907016},
abstract = {Each edition of the challenge on Detection and Classification of Acoustic Scenes and Events DCASE contained several tasks involving sound event detection in different setups. DCASE 2017 presented participants with three such tasks, each having specific datasets and detection requirements: Task 2, in which target sound events were very rare in both training and testing data, Task 3 having overlapping events annotated in real-life audio, and Task 4, in which only weakly labeled data were available for training. In this paper, we present three tasks, including the datasets and baseline systems, and analyze the challenge entries for each task. We observe the popularity of methods using deep neural networks, and the still widely used mel frequency-based representations, with only few approaches standing out as radically different. Analysis of the systems behavior reveals that task-specific optimization has a big role in producing good performance; however, often this optimization closely follows the ranking metric, and its maximization/minimization does not result in universally good performance. We also introduce the calculation of confidence intervals based on a jackknife resampling procedure to perform statistical analysis of the challenge results. The analysis indicates that while the 95% confidence intervals for many systems overlap, there are significant differences in performance between the top systems and the baseline for all tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {992–1006},
numpages = {15}
}

@article{10.1109/TASLP.2019.2906427,
author = {Chetupalli, Srikanth Raj and Sreenivas, Thippur V.},
title = {Late Reverberation Cancellation Using Bayesian Estimation of Multi-Channel Linear Predictors and Student's t-Source Prior},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2906427},
doi = {10.1109/TASLP.2019.2906427},
abstract = {Multi-channel linear prediction MCLP can model the late reverberation in the short-time Fourier transform domain using a delayed linear predictor and the prediction residual is taken as the desired early reflection component. Traditionally, a Gaussian source model with time-dependent precision inverse of variance is considered for the desired signal. In this paper, we propose a Student's t-distribution model for the desired signal, which is realized as a Gaussian source with a Gamma distributed precision. Further, since the choice of a proper MCLP order is critical, we also incorporate a Gaussian distribution prior for the prediction coefficients and a higher order. We consider a batch estimation scenario and develop variational Bayes expectation maximization VBEM algorithm for joint posterior inference and hyper-parameter estimation. This has lead to more accurate and robust estimation of the late reverb component and hence its cancellation, benefitting the desired residual signal estimation. Along with these stochastic models, we formulate single channel output MISO and multi channel output MIMO schemes using shared priors for the desired signal precision and the estimated MCLP coefficients at each microphone. Experiments using real room impulse responses show improved late reverberation suppression with the proposed VBEM approach over the traditional methods, for different room conditions. Additionally, we achieve a sparse coefficient vector for the MCLP avoiding the criticality of manually choosing the model order. The MIMO formulation is easily extended to include spatial filtering of the enhanced signals, which further improves the estimation of the desired signal.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1007–1018},
numpages = {12}
}

@article{10.1109/TASLP.2019.2892895,
author = {Winter, Fiete and Schultz, Frank and Firtha, Gergely and Spors, Sascha},
title = {A Geometric Model for Prediction of Spatial Aliasing in 2.5D Sound Field Synthesis},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2892895},
doi = {10.1109/TASLP.2019.2892895},
abstract = {The avoidance of spatial aliasing is a major challenge in the practical implementation of sound field synthesis. Such methods aim at a physically accurate reconstruction of a desired sound field inside a target region using a finite ensemble of loudspeakers. In the past, different theoretical treatises of the inherent spatial sampling process led to anti-aliasing criteria for simple loudspeaker array arrangements, e.g., lines and circles, and fundamental sound fields, e.g., plane and spherical waves. Many criteria were independent of the listener's position inside the target region. Within this paper, a geometrical framework based on ray-approximation of the underlying synthesis problem is proposed. Unlike former approaches, this model predicts spatial aliasing artefacts for arbitrary convex loudspeaker arrays and as a function of the listening position and the desired sound field. Anti-aliasing criteria for distinct listening positions and extended listening areas are formulated based on the established predictions. For validation, the model is applied to different analytical sound field synthesis approaches: The predicted spatial structure of the spatial aliasing agrees with numerical simulation of the synthesised sound fields. Moreover, it is shown within this framework, that the active prioritization of a control region using so-called local sound field synthesis approaches does indeed reduce spatial aliasing artefacts. For the scenario under investigation, a method for local wave field synthesis achieves an artefact-free synthesis up to a frequency which is between 2.9 and 17.3 times as high as for conventional wave field synthesis.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1031–1046},
numpages = {16}
}

@article{10.1109/TASLP.2019.2908279,
author = {Payal, Shreyas Srikanth and Mathews, V John and Button, Douglas J. and Iyer, Ajay and Lambert, Russell H. and Hutchings, Jeffrey and Azpicueta-Ruiz, Luis A.},
title = {Equalization of Nonlinear Propagation Distortion in Cylindrical Waveguides},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2908279},
doi = {10.1109/TASLP.2019.2908279},
abstract = {A model-based pre-equalizer for propagation-induced distortion of acoustic waveforms in an air-filled waveguide at high sound pressure levels is developed. The nonlinear distortions introduced by propagation are modeled using Burgers propagation model. This equalizer is stimulus-independent and mitigates nonlinear distortion at some predefined distance in the waveguide. The pre-equalizer digitally propagates the waveform backwards using a sign-inverted propagation model. The output of the pre-equalizer is fed to the waveguide. When the forward model completely characterizes the waveguide, the waveforms arriving at the chosen destination will be identical to a delayed version of the input signal fed to the pre-equalizer. Experimental evaluations employing sinusoidal and multitone stimuli demonstrated approximately a two-fold reduction in the intermodulation distortion and a four-fold reduction in total harmonic distortion at low frequencies. These results represented a two-fold improvement over a model-based method available in the literature.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1072–1084},
numpages = {13}
}

@article{10.1109/TASLP.2019.2908057,
author = {Porschmann, Christoph and Arend, Johannes M. and Brinkmann, Fabian},
title = {Directional Equalization of Sparse Head-Related Transfer Function Sets for Spatial Upsampling},
year = {2019},
issue_date = {June 2019},
publisher = {IEEE Press},
volume = {27},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2908057},
doi = {10.1109/TASLP.2019.2908057},
abstract = {Acquiring decent full-spherical sets of head-related transfer functions HRTFs based on a small number of measurements is highly desirable. For spatial upsampling, HRTF interpolation in the spatially continuous spherical harmonics SH domain is a common approach. However, the number of measured HRTFs limits the assessable SH order, resulting in order-limited HRTFs when transformed to the SH domain. Thus, the SH representation of sparse HRTF sets shows restricted spatial resolution and suffers from order-limitation errors. We present a method that reduces these errors by a directional equalization prior to the SH transform. This is done by a spectral division of each HRTF with a corresponding directional rigid sphere transfer function. The processing removes direction-dependent temporal and spectral components and, therefore, significantly reduces the spatial complexity of the HRTF set, allowing for an enhanced interpolation of HRTFs at reduced SH orders. Spatial upsampling is achieved by an inverse SH transform on an arbitrary dense sampling grid. A subsequent de-equalization by a spectral multiplication with the rigid sphere transfer function recovers the energy in higher spatial orders that was not inherent in the sparse HRTF set. For evaluation, HRTFs were calculated for various limited orders from sparse datasets and compared to a reference. The results show that the proposed method clearly outperforms common SH interpolation of HRTF spectra regarding the overall spectral and temporal structure as well as modeled localization performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1060–1071},
numpages = {12}
}

@article{10.1109/TASLP.2019.2903288,
author = {Kumar, Anurendra and Guha, Tanaya and Ghosh, Prasanta Kumar},
title = {Dirichlet Latent Variable Model: A Dynamic Model Based on Dirichlet Prior for Audio Processing},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2903288},
doi = {10.1109/TASLP.2019.2903288},
abstract = {We propose a dynamic latent variable model for learning latent bases from time varying, non-negative data. We take a probabilistic approach to modeling the temporal dependence in data by introducing a dynamic Dirichlet prior—a Dirichlet distribution with dynamic parameters. This new distribution allows us to assure non-negativity and avoid intractability when sequential updates are performed otherwise encountered in using Dirichlet prior. We refer to the proposed model as the Dirichlet latent variable model DLVM. We develop an expectation maximization algorithm for the proposed model, and also derive a maximum a posteriori estimate of the parameters. Furthermore, we connect the proposed DLVM to two popular latent basis learning methods—probabilistic latent component analysis PLCA and non-negative matrix factorization NMF. We show that 1 PLCA is a special case of our DLVM, and 2 DLVM can be interpreted as a dynamic version of NMF. The usefulness of DLVM is demonstrated for three audio processing applications—speaker source separation, denoising, and bandwidth expansion. To this end, a new algorithm for source separation is also proposed. Through extensive experiments on benchmark databases, we show that the proposed model outperforms several relevant existing methods in all three applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {919–931},
numpages = {13}
}

@article{10.1109/TASLP.2019.2905167,
author = {Koriyama, Tomoki and Kobayashi, Takao},
title = {Statistical Parametric Speech Synthesis Using Deep Gaussian Processes},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2905167},
doi = {10.1109/TASLP.2019.2905167},
abstract = {This paper proposes a framework of speech synthesis based on deep Gaussian processes DGPs, which is a deep architecture model composed of stacked Bayesian kernel regressions. In this method, we train a statistical model of transformation from contextual features to speech parameters in a similar manner to deep neural network DNN-based speech synthesis. To apply DGPs to a statistical parametric speech synthesis framework, our framework uses an approximation method, doubly stochastic variational inference, which is suitable for an arbitrary amount of data. Since the training of DGPs is based on the marginal likelihood that takes into account not only data fitting, but also model complexity, DGPs are less vulnerable to overfitting compared with DNNs. In experimental evaluations, we investigated a performance comparison of the proposed DGP-based framework with a feedforward DNN-based one. Subjective and objective evaluation results showed that our DGP framework yielded a higher mean opinion score and lower acoustic feature distortions than the conventional framework.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {948–959},
numpages = {12}
}

@article{10.1109/TASLP.2019.2903276,
author = {Elisei-Iliescu, Camelia and Paleologu, Constantin and Benesty, Jacob and Stanciu, Cristian and Anghel, Cristian and Ciochina, Silviu},
title = {Recursive Least-Squares Algorithms for the Identification of Low-Rank Systems},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2903276},
doi = {10.1109/TASLP.2019.2903276},
abstract = {The recursive least-squares RLS adaptive filter is an appealing choice in many system identification problems. The main reason behind its popularity is its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the identification of long length impulse responses, like in echo cancellation. Computationally efficient versions of the RLS algorithm, like those based on the dichotomous coordinate descent DCD iterations or QR decomposition techniques, reduce the complexity, but still have to face the challenges related to long length adaptive filters e.g., convergence/tracking capabilities. In this paper, we focus on a different approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. In other words, a high-dimension system identification problem is reformulated in terms of low-dimension problems, which are combined together. This approach was recently addressed in terms of the Wiener filter, showing appealing features for the identification of low-rank systems, like real-world echo paths. In this paper, besides the development of the RLS algorithm based on this approach, we also propose a variable regularized version of this algorithm using the DCD method to reduce the complexity, with improved robustness to double-talk. Simulations are performed in the context of echo cancellation and the results indicate the good performance of these algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {903–918},
numpages = {16}
}

@article{10.1109/TASLP.2019.2895241,
author = {Cohen, Israel and Benesty, Jacob and Chen, Jingdong},
title = {Differential Kronecker Product Beamforming},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2895241},
doi = {10.1109/TASLP.2019.2895241},
abstract = {Differential beamformers have attracted much interest over the past few decades. In this paper, we introduce differential Kronecker product beamformers that exploit the structure of the steering vector to perform beamforming differently from the well-known and studied conventional approach. We consider a class of microphone arrays that enable to decompose the steering vector as a Kronecker product of two steering vectors of smaller virtual arrays. In the proposed approach, instead of directly designing the differential beamformer, we break it down following the decomposition of the steering vector, and show how to derive differential beamformers using the Kronecker product formulation. As demonstrated, the Kronecker product decomposition facilitates further flexibility in the design of differential beamformers and in the tradeoff control between the directivity factor and the white noise gain.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {892–902},
numpages = {11}
}

@article{10.1109/TASLP.2019.2907015,
author = {Shimada, Kazuki and Bando, Yoshiaki and Mimura, Masato and Itoyama, Katsutoshi and Yoshii, Kazuyoshi and Kawahara, Tatsuya},
title = {Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2907015},
doi = {10.1109/TASLP.2019.2907015},
abstract = {This paper describes multichannel speech enhancement for improving automatic speech recognition ASR in noisy environments. Recently, the minimum variance distortionless response MVDR beamforming has widely been used because it works well if the steering vector of speech and the spatial covariance matrix SCM of noise are given. To estimating such spatial information, conventional studies take a supervised approach that classifies each time-frequency TF bin into noise or speech by training a deep neural network DNN. The performance of ASR, however, is degraded in an unknown noisy environment. To solve this problem, we take an unsupervised approach that decomposes each TF bin into the sum of speech and noise by using multichannel nonnegative matrix factorization MNMF. This enables us to accurately estimate the SCMs of speech and noise not from observed noisy mixtures but from separated speech and noise components. In this paper, we propose online MVDR beamforming by effectively initializing and incrementally updating the parameters of MNMF. Another main contribution is to comprehensively investigate the performances of ASR obtained by various types of spatial filters, i.e., time-invariant and variant versions of MVDR beamformers and those of rank-1 and full-rank multichannel Wiener filters, in combination with MNMF. The experimental results showed that the proposed method outperformed the state-of-the-art DNN-based beamforming method in unknown environments that did not match training data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {960–971},
numpages = {12}
}

@article{10.1109/TASLP.2019.2901643,
author = {Ibarrola, Francisco Javier and Spies, Ruben Daniel and Persia, Leandro Ezequiel Di},
title = {Switching Divergences for Spectral Learning in Blind Speech Dereverberation},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2901643},
doi = {10.1109/TASLP.2019.2901643},
abstract = {When recorded in an enclosed room, a sound signal will most certainly get affected by reverberation. This not only undermines audio quality, but also poses a problem for many human-machine interaction technologies that use speech as their input. In this paper, a new blind, two-stage dereverberation approach based in a generalized $beta$-divergence as a fidelity term over a non-negative representation is proposed. The first stage consists of learning the spectral structure of the signal solely from the observed spectrogram, while the second stage is devoted to model reverberation. Both steps are taken by minimizing a cost function in which the aim is put either in constructing a dictionary or a good representation by changing the divergence involved. In addition, an approach for finding an optimal fidelity parameter for dictionary learning is proposed. An algorithm for implementing the proposed method is described and tested against state-of-the-art methods. Results show improvements for both artificial reverberation and real recordings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {881–891},
numpages = {11}
}

@article{10.1109/TASLP.2019.2904790,
author = {Jancovic, Peter and Kokuer, Munevver},
title = {Bird Species Recognition Using Unsupervised Modeling of Individual Vocalization Elements},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2904790},
doi = {10.1109/TASLP.2019.2904790},
abstract = {This paper investigates acoustic modeling for recognition of bird species from audio field recordings. First, the acoustic scene is decomposed into isolated segments, corresponding to detected sinusoids. Each segment is represented by a sequence of the frequency and normalized magnitude values of the sinusoid. The temporal evolution of these features is modeled using hidden Markov models HMMs. A novel method for an unsupervised modeling of individual bird vocalization elements is proposed. The element models are initialized using HMM-based clustering and then further trained using an iterative maximum likelihood label re-assignment procedure. State duration modeling, performed in a post-recognition stage, is explored. Finally, we developed a hybrid deep neural network—hidden Markov model. The developed acoustic models are employed for bird species identification, detection of specific species, and recognition of multiple bird species vocalizing in a given recording. The detection system employs score normalization. Recognition of multiple bird species is performed based on maximizing the likelihood of a set of segments on a subset of bird species models, with penalization based on Bayesian information criterion applied. Experimental evaluations are performed on more than 37 h of sound field recordings, containing vocalizations of 48 bird species, plus more than 16 h of non-bird sound recordings. Using 3 s of the detected signal, the best system achieved: identification accuracy of 98.7%, detection with the equal error rate of 2.7%, and recognition accuracy of 97.3% and 95.4% when vocalizations of multiple bird species are present, with the number of bird species known and estimated, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {932–947},
numpages = {16}
}

@article{10.1109/TASLP.2019.2904839,
author = {Widmark, Simon},
title = {Causal MSE-Optimal Filters for Personal Audio Subject to Constrained Contrast},
year = {2019},
issue_date = {May 2019},
publisher = {IEEE Press},
volume = {27},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2904839},
doi = {10.1109/TASLP.2019.2904839},
abstract = {A novel design method that generates causal pre-compensation filters is formulated. The resulting filters are constrained with respect to the amount of acoustic contrast they generate and are intended to be used for personal audio. The proposed method provides a more direct method for trading bright zone behavior against acoustic contrast as compared to other causal methods available. It also provides improved control over the temporal properties of the resulting filters as compared to the pre-existing non-causal methods. The resulting filters are analyzed by means of simulations, based on measured impulse responses of the sound-system–room interactions. The results of the simulations are compared to simulations of a frequency-domain optimal method with comparable objective, as proposed by Cai et al. and the results of the comparison are explained using the design equations. It is shown that the proposed method is viable, but that unattainable contrasts have a detrimental impact on the spectral bright zone behavior. A few different strategies for dealing with this problem are also proposed. It is demonstrated that the detrimental effect of increasingly strict causality constraints mainly concerns the lower frequency bright zone behavior in the system under investigation, but that the very highest attainable contrast levels may also be reduced somewhat.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {972–987},
numpages = {16}
}

@article{10.1109/TASLP.2019.2899517,
author = {Wang, Xianghui and Cohen, Israel and Chen, Jingdong and Benesty, Jacob},
title = {On Robust and High Directive Beamforming With Small-Spacing Microphone Arrays for Scattered Sources},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2899517},
doi = {10.1109/TASLP.2019.2899517},
abstract = {This paper is devoted to beamforming with small-spacing microphone arrays for processing broadband and scattered acoustic sources. It presents a maximum diffuse noise gain MDNG beamformer in this context using the joint diagonalization technique, which is effective in suppressing diffuse and directional noise, but at a price of low white noise gain WNG. We also introduce a maximum WNG MWNG beamformer, which is robust to the array imperfections, but paying a price of sacrificing the diffuse noise gain DNG. To make a tradeoff between WNG and DNG so that the beamformer, on the one hand, can achieve high directivity and, on the other hand, is robust to implement, we propose a generalized MDNG beamformer, which includes both the MDNG and MWNG beamformers as particular cases. Simulations are conducted to illustrate the properties and advantages of the proposed beamformers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {842–852},
numpages = {11}
}

@article{10.1109/TASLP.2019.2895254,
author = {Kong, Qiuqiang and Xu, Yong and Sobieraj, Iwona and Wang, Wenwu and Plumbley, Mark D.},
title = {Sound Event Detection and Time–Frequency Segmentation from Weakly Labelled Data},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2895254},
doi = {10.1109/TASLP.2019.2895254},
abstract = {Sound event detection SED aims to detect when and recognize what sound events happen in an audio clip. Many supervised SED algorithms rely on strongly labelled data that contains the onset and offset annotations of sound events. However, many audio tagging datasets are weakly labelled, that is, only the presence of the sound events is known, without knowing their onset and offset annotations. In this paper, we propose a time–frequency T–F segmentation framework trained on weakly labelled data to tackle the sound event detection and separation problem. In training, a segmentation mapping is applied on a T–F representation, such as log mel spectrogram of an audio clip to obtain T–F segmentation masks of sound events. The T–F segmentation masks can be used for separating the sound events from the background scenes in the T–F domain. Then, a classification mapping is applied on the T–F segmentation masks to estimate the presence probabilities of the sound events. We model the segmentation mapping using a convolutional neural network and the classification mapping using a global weighted rank pooling. In SED, predicted onset and offset times can be obtained from the T–F segmentation masks. As a byproduct, separated waveforms of sound events can be obtained from the T–F segmentation masks. We remixed the DCASE 2018 Task 1 acoustic scene data with the DCASE 2018 Task 2 sound events data. When mixing under 0 dB, the proposed method achieved F1 scores of 0.534, 0.398, and 0.167 in audio tagging, frame-wise SED and event-wise SED, outperforming the fully connected deep neural network baseline of 0.331, 0.237, and 0.120, respectively. In T–F segmentation, we achieved an F1 score of 0.218, where previous methods were not able to do T–F segmentation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {777–787},
numpages = {11}
}

@article{10.1109/TASLP.2019.2898818,
author = {Lin, Shoufeng},
title = {Robust Pitch Estimation and Tracking For Speakers Based on Subband Encoding and The Generalized Labeled Multi-Bernoulli Filter},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2898818},
doi = {10.1109/TASLP.2019.2898818},
abstract = {This paper proposes a new pitch estimator and a novel pitch tracker for speakers. We first decompose the sound signal into subbands using an auditory filterbank, assuming time–frequency sparsity of human speech. Instead of directly selecting the number of subbands according to experience, we propose a novel frequency coverage metric to derive the number of subbands and the center frequencies of the filterbank. The subband signals are then encoded inspired by the computational auditory scene analysis approach, and the normalized autocorrelations are calculated for pitch estimation. To suppress spurious errors and track the speaker identity, the temporal continuity constraint is exploited and a generalized labeled multi-Bernoulli filter is adapted for pitch tracking, where we use a novel pitch state transition model based on the Ornstein–Uhlenbeck process, and the measurement-driven birth model for adaptive new births of pitch targets. Experimental evaluations with various additive noises demonstrate that the proposed methods have achieved better accuracy compared with several state-of-the-art pitch estimation methods in most studied scenarios. Tests using real recordings in a reverberant room also show that the proposed method is robust against reverberation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {827–841},
numpages = {15}
}

@article{10.1109/TASLP.2019.2892234,
author = {Schepker, Henning and Nordholm, Sven Erik and Tran, Linh Thi Thuc and Doclo, Simon},
title = {Null-Steering Beamformer-Based Feedback Cancellation for Multi-Microphone Hearing Aids With Incoming Signal Preservation},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2892234},
doi = {10.1109/TASLP.2019.2892234},
abstract = {In hearing aids, acoustic feedback occurs due to the coupling between the hearing aid loudspeaker and microphones. In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphones. While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter converges to an optimal solution that is typically biased due to the closed-loop acoustical system of the hearing aid. In order to avoid the adaptation to a biased optimal solution, in this paper we propose to use a fixed beamformer to cancel the acoustic feedback contribution for an earpiece with multiple integrated microphones and loudspeakers. By steering a spatial null in the direction of the hearing aid loudspeaker, we show that theoretically perfect feedback cancellation can be achieved. While previous null-steering beamforming approaches did not control for distortions of the incoming signal, in this paper we propose to incorporate a constraint based on the relative transfer function RTF of the incoming signal, aiming to perfectly preserve this signal. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min–max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental results using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha show that the proposed fixed null-steering beamformer using the RTF-based constraint provides a reduction of the acoustic feedback and substantially increases the added stable gain while preserving the incoming signal. This can even be achieved for unknown acoustic feedback paths and incoming signal directions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {679–691},
numpages = {13}
}

@article{10.1109/TASLP.2019.2893499,
author = {Zhou, Mantong and Huang, Minlie and Zhu, Xiaoyan},
title = {Story Ending Selection by Finding Hints From Pairwise Candidate Endings},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2893499},
doi = {10.1109/TASLP.2019.2893499},
abstract = {The ability of story comprehension is a strong indicator of natural language understanding. Recently, Story Cloze Test has been introduced as a new task of machine reading comprehension, i.e., selecting a correct ending from two candidate endings given a four-sentence story context. Most existing methods for Story Cloze Test are essentially matching-based that operate by comparing an individual ending with a given context, therefore suffering from the evidence bias issue: both candidate endings can obtain supporting evidence from the story context, which misleads the classifier to choose an incorrect ending. To address this issue, we present a novel idea to improve story comprehension by utilizing the hints that are obtained through comparing two candidate endings. The proposed model firstly anticipates a feature vector for a possible ending solely based on the context, and then refines the feature prediction using the hints which encode the difference between two candidates. The candidate ending whose feature vector is more similar to the predicted ending vector is regarded as correct. Experimental results demonstrate that our approach can alleviate the evidence bias issue and improve story comprehension.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {719–729},
numpages = {11}
}

@article{10.1109/TASLP.2019.2892232,
author = {Deng, Dong and Jing, Liping and Yu, Jian and Sun, Shaolong and Ng, Michael K.},
title = {Sentiment Lexicon Construction With Hierarchical Supervision Topic Model},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2892232},
doi = {10.1109/TASLP.2019.2892232},
abstract = {In this paper, we propose a novel hierarchical supervision topic model to construct a topic-adaptive sentiment lexicon TaSL for higher-level classification tasks. It is widely recognized that sentiment lexicon as a useful prior knowledge is crucial in sentiment analysis or opinion mining. However, many existing sentiment lexicons are constructed ignoring the variability of the sentiment polarities of words in different topics or domains. For example, the word “amazing” can refer to causing great surprise or wonder but can also refer to very impressive and excellent. In TaSL, we solve this issue by jointly considering the topics and sentiments of words. Documents are represented by multiple pairs of topics and sentiments, where each pair is characterized by a multinomial distribution over words. Meanwhile, this generating process is supervised under hierarchical supervision information of documents and words. The main advantage of TaSL is that the sentiment polarity of each word in different topics can be sufficiently captured. This model is beneficial to construct a domain-specific sentiment lexicon and then effectively improve the performance of sentiment classification. Extensive experimental results on four publicly available datasets, MR, OMD, semEval13A, and semEval16B were presented to demonstrate the usefulness of the proposed approach. The results have shown that TaSL performs better than the existing manual sentiment lexicon MPQA, the topic model based domain-specific lexicon ssLDA, the expanded lexiconsWeka-ED, Weka-STS, NRC, Liu's, and deep neural network based lexicons nnLexicon, HIT, HSSWE.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {704–718},
numpages = {15}
}

@article{10.1109/TASLP.2019.2896437,
author = {Tuan, Yi-Lin and Lee, Hung-Yi},
title = {Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2896437},
doi = {10.1109/TASLP.2019.2896437},
abstract = {Sequence generative adversarial networks SeqGAN have been used to improve conditional sequence generation tasks, for example, chit-chat dialogue generation. To stabilize the training of SeqGAN, Monte Carlo tree search MCTS or reward at every generation step REGS is used to evaluate the goodness of a generated subsequence. MCTS is computationally intensive, but the performance of REGS is worse than MCTS. In this paper, we propose stepwise GAN StepGAN, in which the discriminator is modified to automatically assign scores quantifying the goodness of each subsequence at every generation step. StepGAN has significantly less computational costs than MCTS. We demonstrate that StepGAN outperforms previous GAN-based methods on both synthetic experiment and chit-chat dialogue generation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {788–798},
numpages = {11}
}

@article{10.1109/TASLP.2019.2894554,
author = {Yu, Jianguo and Markov, Konstantin and Matsui, Tomoko},
title = {Articulatory and Spectrum Information Fusion Based on Deep Recurrent Neural Networks},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2894554},
doi = {10.1109/TASLP.2019.2894554},
abstract = {Many studies have shown that articulatory features can significantly improve the performance of automatic speech recognition systems. Unfortunately, such features are not available at recognition time. There are two main approaches to solve this problem: a feature-based approach, the most popular example of which is the acoustic-to-articulatory inversion, where the missing articulatory features are generated from the speech signal, and a model-based approach, where articulatory information is embedded in the model structure and parameters in a way that allows recognition using only acoustic features. In this paper, we propose two new methods to integrate articulatory information into a phoneme recognition system. One of them is feature based, and the other is model based. In both cases, the underlying acoustic model AM is a deep neural networks-hidden Markov model DNN-HMM hybrid. In the feature-based method, the articulatory inversion DNN and the acoustic model DNN are trained jointly using a linear combination of their loss functions. In the model-based method, we utilize the generalized distillation framework to train the AM DNN. In this case, first, a teacher DNN is trained on both the acoustic and articulatory features, and then its outputs are used as additional targets during the AM DNN training with acoustic features only. A 7-fold cross-validation experiments using 42 speakers from the XRMB database showed that both the proposed methods provide about 22% to 25% performance improvement with respect to the DNN acoustic model trained with acoustic features only.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {742–752},
numpages = {11}
}

@article{10.1109/TASLP.2019.2899494,
author = {Quan, Zhe and Wang, Zhi-Jie and Le, Yuquan and Yao, Bin and Li, Kenli and Yin, Jian},
title = {An Efficient Framework for Sentence Similarity Modeling},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2899494},
doi = {10.1109/TASLP.2019.2899494},
abstract = {Sentence similarity modeling lies at the core of many natural language processing applications, and thus has received much attention. Owing to the success of word embeddings, recently, popular neural network methods achieved sentence embedding. Most of them focused on learning semantic information and modeling it as a continuous vector, yet the syntactic information of sentences has not been fully exploited. On the other hand, prior works have shown the benefits of structured trees that include syntactic information, while few methods in this branch utilized the advantages of word embeddings and another powerful technique—attention weight mechanism. This paper suggests to absorb their advantages by merging these techniques in a unified structure, dubbed as attention constituency vector tree ACVT. Meanwhile, this paper develops a new tree kernel, known as ACVT kernel, which is tailored for sentence similarity measure based on the proposed structure. The experimental results, based on 19 widely used semantic textual similarity datasets, demonstrate that our model is effective and competitive, when compared against state-of-the-art models. Additionally, the experimental results validate that many attention weight mechanisms and word embedding techniques can be seamlessly integrated into our model, demonstrating the robustness and universality of our model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {853–865},
numpages = {13}
}

@article{10.1109/TASLP.2019.2895969,
author = {Abel, Johannes and Fingscheidt, Tim},
title = {Sinusoidal-Based Lowband Synthesis for Artificial Speech Bandwidth Extension},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2895969},
doi = {10.1109/TASLP.2019.2895969},
abstract = {Conventional narrowband NB telephony suffers from limited acoustic bandwidth at the receiver side, leading to degraded speech quality and intelligibility. In this paper, artificial speech bandwidth extension ABE of NB speech toward missing frequencies below about 300 Hz low-frequency band, LB is proposed to enhance the speech quality. The LB-ABE in this paper is employed together with a preexisting ABE toward high-frequency components to obtain spectrally balanced speech signals. In an instrumental quality assessment, the spectral distance in the LB was improved by more than 5 dB compared to NB speech. In a subjective listening test, the gap of speech quality between wideband and NB speech was significantly reduced when employing the proposed ABE toward low frequencies. The LB extension was found to further improve the preexisting ABE toward higher frequencies by a significant 0.26 CMOS points.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {765–776},
numpages = {12}
}

@article{10.1109/TASLP.2019.2900910,
author = {Lubis, Nurul and Sakti, Sakriani and Yoshino, Koichiro and Nakamura, Satoshi},
title = {Positive Emotion Elicitation in Chat-Based Dialogue Systems},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2900910},
doi = {10.1109/TASLP.2019.2900910},
abstract = {We aim to draw on an important overlooked potential of affective dialogue systems—their application to promote positive emotional states, similar to that of emotional support between humans. This can be achieved by eliciting a more positive emotional valence throughout a dialogue system interaction, i.e., positive emotion elicitation. Existing works on emotion elicitation have not yet paid attention to the emotional benefit for the users. Moreover, a positive emotion elicitation corpus does not yet exist despite the growing number of emotion-rich corpora. Towards this goal, first, we propose a response retrieval approach for positive emotion elicitation by utilizing examples of emotion appraisal from a dialogue corpus. Second, we efficiently construct a corpus using the proposed retrieval method, by replacing responses in a dialogue with those that elicit a more positive emotion. We validate the corpus through crowdsourcing to ensure its quality. Finally, we propose a novel neural network architecture for an emotion-sensitive neural chat-based dialogue system, optimized on the constructed corpus to elicit positive emotion. Objective and subjective evaluations show that the proposed methods result in dialogue responses that are more natural and elicit a more positive emotional response. Further analyses of the results are discussed in this paper.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {866–877},
numpages = {12}
}

@article{10.1109/TASLP.2019.2895973,
author = {Itturriet, Fabio Pires and Costa, Marcio Holsbach},
title = {Perceptually Relevant Preservation of Interaural Time Differences in Binaural Hearing Aids},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2895973},
doi = {10.1109/TASLP.2019.2895973},
abstract = {This paper presents a noise reduction method with perceptually relevant preservation of the interaural time difference ITD of the residual noise in binaural hearing aids. The interaural coherence IC concept, previously applied to the multichannel Wiener filter MWF for preservation of the spatial subjective sensation of diffuse noise fields, is proposed here to both preserve and emphasize the ITD binaural cues of a directional acoustic noise source. It is demonstrated that the previously developed MWF-ITD technique may decrease the original IC magnitude of the processed noise, consequently increasing the variance of the interaural phase difference IPD of the output signals. It is shown that the MWF-IC technique simultaneously minimizes a nonlinear function of the difference between input and output IPD, which is strictly related to ITD, and preserves the natural coherence of directional noise captured by the reference microphones. Objective measures and psychoacoustic experiments corroborate the theoretical findings, showing that the MWF-IC technique provides relevant noise reduction, while preserving the original ITD subjective perception and original lateralization for a directional noise source. These results are especially relevant for hearing aid designers, since they indicate MWF-IC as a noise reduction technique that provides residual noise spatial preservation for both diffuse and directional noise sources in frequencies below 1.5 kHz.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {753–764},
numpages = {12}
}

@article{10.1109/TASLP.2018.2887337,
author = {Zhao, Ziyue and Liu, Huijun and Fingscheidt, Tim},
title = {Convolutional Neural Networks to Enhance Coded Speech},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2887337},
doi = {10.1109/TASLP.2018.2887337},
abstract = {Enhancing coded speech suffering from far-end acoustic background noise, quantization noise, and potentially transmission errors is a challenging task. In this paper, we propose two postprocessing approaches applying convolutional neural networks either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time-domain approach follows an end-to-end fashion, whereas the cepstral domain approach uses analysis–synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves perceptual evaluation of speech quality by up to 0.25 mean opinion score listening quality objective points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec. In a subjective comparison category rating listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to legacy G.711, even better than uncoded speech with statistical significance. The source code for the cepstral domain approach to enhance G.711-coded speech is made available.11https://github.com/ifnspaml/Enhancement-Coded-Speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {663–678},
numpages = {16}
}

@article{10.1109/TASLP.2019.2898816,
author = {Lotfian, Reza and Busso, Carlos},
title = {Curriculum Learning for Speech Emotion Recognition From Crowdsourced Labels},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2898816},
doi = {10.1109/TASLP.2019.2898816},
abstract = {This study introduces a method to design a curriculum for machine-learning to maximize the efficiency during the training process of deep neural networks DNNs for speech emotion recognition. Previous studies in other machine-learning problems have shown the benefits of training a classifier following a curriculum where samples are gradually presented in increasing level of difficulty. For speech emotion recognition, the challenge is to establish a natural order of difficulty in the training set to create the curriculum. We address this problem by assuming that, ambiguous samples for humans are also ambiguous for computers. Speech samples are often annotated by multiple evaluators to account for differences in emotion perception across individuals. While some sentences with clear emotional content are consistently annotated, sentences with more ambiguous emotional content present important disagreement between individual evaluations. We propose to use the disagreement between evaluators as a measure of difficulty for the classification task. We propose metrics that quantify the inter-evaluation agreement to define the curriculum for regression problems and binary and multi-class classification problems. The experimental results consistently show that relying on a curriculum based on agreement between human judgments leads to statistically significant improvements over baselines trained without a curriculum.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {815–826},
numpages = {12}
}

@article{10.1109/TASLP.2019.2894329,
author = {Richter, Jan-Gerrit and Fels, Janina},
title = {On the Influence of Continuous Subject Rotation During High-Resolution Head-Related Transfer Function Measurements},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2894329},
doi = {10.1109/TASLP.2019.2894329},
abstract = {In recent years, the required time for high resolution individual head-related transfer function HRTF measurements has been reduced by the use of microphone or loudspeaker arrays and with parallelization of measurement signals. While a short measurement time is beneficial during HRTF measurement of any resolution, it is essential for the acquisition of high resolution individual HRTF measurements since subjects cannot remain motionless over extended periods of time. This decrease in measurement time can be achieved by using continuous rotation during the measurement, instead of step-wise subject positioning. This paper examines the influence of such continuous measurement rotation with sine sweep measurements and the necessary subsequent post-processing. The goal is to identify a rotation speed, from a predefined set of speeds, that does not have an audible influence on the measured HRTF itself. To this end, a subjective and objective comparison between the two measurement modes is presented in this paper. Five HRTF measurements of an artificial head are acquired using different rotation speeds. They are compared objectively to a reference measurement of the same head performed with step-wise measurement rotation in the same measurement setup. A subjective listening experiment examines whether the found differences between the measurements do result in an audible difference. The objective evaluation showed expected trends regarding the dependency of the error on the measurement speed and is used to validate the used signal post-processing. The subjective evaluation showed that with a rotation of 3.8$^circ$/s no audible differences to the reference are present.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {730–741},
numpages = {12}
}

@article{10.1109/TASLP.2019.2892241,
author = {Li, Zeng-Xi and Song, Yan and Dai, Li-Rong and McLoughlin, Ian},
title = {Listening and Grouping: An Online Autoregressive Approach for Monaural Speech Separation},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2892241},
doi = {10.1109/TASLP.2019.2892241},
abstract = {This paper proposes an autoregressive approach to harness the power of deep learning for multi-speaker monaural speech separation. It exploits a causal temporal context in both mixture and past estimated separated signals and performs online separation that is compatible with real-time applications. The approach adopts a learned listening and grouping architecture motivated by computational auditory scene analysis, with a grouping stage that effectively addresses the label permutation problem at both frame and segment levels. Experimental results on the WSJ0-2mix benchmark show that the new approach can achieve better signal-to-distortion ratio and perceptual evaluation of speech quality scores than most of the state-of-the-art methods for both closed-set and open-set evaluations, even methods that exploit whole-utterance statistics for separation. It achieves this while requiring fewer model parameters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {692–703},
numpages = {12}
}

@article{10.1109/TASLP.2019.2894909,
author = {Dionelis, Nikolaos and Brookes, Mike},
title = {Modulation-Domain Kalman Filtering for Monaural Blind Speech Denoising and Dereverberation},
year = {2019},
issue_date = {April 2019},
publisher = {IEEE Press},
volume = {27},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2894909},
doi = {10.1109/TASLP.2019.2894909},
abstract = {We describe a monaural speech enhancement algorithm based on modulation-domain Kalman filtering to blindly track the time–frequency log-magnitude spectra of speech and reverberation. We propose an adaptive algorithm that performs blind joint denoising and dereverberation, while accounting for the inter-frame speech dynamics, by estimating the posterior distribution of the speech log-magnitude spectrum given the log-magnitude spectrum of the noisy reverberant speech. The Kalman filter update step models the non-linear relations between the speech, noise, and reverberation log spectra. The Kalman filtering algorithm uses a signal model that takes into account the reverberation parameters of the reverberation time $T_{60}$ and the direct-to-reverberant energy ratio DRR and also estimates and tracks $T_{60}$ and the DRR in every frequency bin to improve the estimation of the speech log spectrum. The proposed algorithm is evaluated in terms of speech quality, speech intelligibility, and dereverberation performance for a range of reverberation parameters and reverberant speech to noise ratios, in different noises, and is also compared to competing denoising and dereverberation techniques. Experimental results using noisy reverberant speech demonstrate the effectiveness of the enhancement algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {799–814},
numpages = {16}
}

@article{10.1109/TASLP.2019.2892235,
author = {Zhang, Jing-Xuan and Ling, Zhen-Hua and Liu, Li-Juan and Jiang, Yuan and Dai, Li-Rong},
title = {Sequence-to-Sequence Acoustic Modeling for Voice Conversion},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2892235},
doi = {10.1109/TASLP.2019.2892235},
abstract = {In this paper, a neural network named sequence-to-sequence ConvErsion NeTwork SCENT is presented for acoustic modeling in voice conversion. At training stage, a SCENT model is estimated by aligning the feature sequences of source and target speakers implicitly using attention mechanism. At the conversion stage, acoustic features and durations of source utterances are converted simultaneously using the unified acoustic model. Mel-scale spectrograms are adopted as acoustic features, which contain both excitation and vocal tract descriptions of speech signals. The bottleneck features extracted from source speech using an automatic speech recognition model are appended as an auxiliary input. A WaveNet vocoder conditioned on Mel-spectrograms is built to reconstruct waveforms from the outputs of the SCENT model. It is worth noting that our proposed method can achieve appropriate duration conversion, which is difficult in conventional methods. Experimental results show that our proposed method obtained better objective and subjective performance than the baseline methods using Gaussian mixture models and deep neural networks as acoustic models. This proposed method also outperformed our previous work, which achieved the top rank in Voice Conversion Challenge 2018. Ablation tests further confirmed the effectiveness of several components in our proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {631–644},
numpages = {14}
}

@article{10.1109/TASLP.2018.2883740,
author = {Zhang, Jiajun and Zhao, Yang and Li, Haoran and Zong, Chengqing},
title = {Attention With Sparsity Regularization for Neural Machine Translation and Summarization},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2883740},
doi = {10.1109/TASLP.2018.2883740},
abstract = {The attention mechanism has become the de facto standard component in neural sequence to sequence tasks, such as machine translation and abstractive summarization. It dynamically determines which parts in the input sentence should be focused on when generating each word in the output sequence. Ideally, only few relevant input words should be attended to at each decoding time step and the attention weight distribution should be sparse and sharp. However, previous methods have no good mechanism to control this attention weight distribution. In this paper, we propose a sparse attention model in which a sparsity regularization term is designed to augment the objective function. We explore two kinds of regularizations: $L_{infty }$-norm regularization and minimum entropy regularization, both of which aim to sharpen the attention weight distribution. Extensive experiments on both neural machine translation and abstractive summarization demonstrate that our proposed sparse attention model can substantially outperform the strong baselines. And the detailed analyses reveal that the final attention distribution indeed becomes sparse and sharp.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {507–518},
numpages = {12}
}

@article{10.1109/TASLP.2018.2889927,
author = {Zhu, Qiaoxi and Coleman, Philip and Qiu, Xiaojun and Wu, Ming and Yang, Jun and Burnett, Ian},
title = {Robust Personal Audio Geometry Optimization in the SVD-Based Modal Domain},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2889927},
doi = {10.1109/TASLP.2018.2889927},
abstract = {Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most “natural” sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching, and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of “occlusion,” improved robustness to regularization, and improved broadband equalization.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {610–620},
numpages = {11}
}

@article{10.1109/TASLP.2018.2889606,
author = {Yi, Jiangyan and Tao, Jianhua and Wen, Zhengqi and Bai, Ye},
title = {Language-Adversarial Transfer Learning for Low-Resource Speech Recognition},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2889606},
doi = {10.1109/TASLP.2018.2889606},
abstract = {The acoustic model trained using the knowledge from the shared hidden layer SHL model outperforms the model trained only by using the target language, especially under low resource conditions. However, the shared features may contain some unnecessary language dependent information. It will degrade the performance of the target model. Therefore, this paper proposes language-adversarial transfer learning to alleviate this problem. Adversarial learning is used to ensure that the shared layers of the SHL-model can learn more language invariant features. Experiments are conducted on IARPA Babel datasets. The results show that the target model trained using the knowledge transferred from the adversarial SHL-model achieves up to 10.1% relative word error rate reduction when compared with the target model trained using the knowledge transferred from the SHL-model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {621–630},
numpages = {10}
}

@article{10.1109/TASLP.2018.2886743,
author = {Dietzen, Thomas and Spriet, Ann and Tirry, Wouter and Doclo, Simon and Moonen, Marc and van Waterschoot, Toon},
title = {Comparative Analysis of Generalized Sidelobe Cancellation and Multi-Channel Linear Prediction for Speech Dereverberation and Noise Reduction},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2886743},
doi = {10.1109/TASLP.2018.2886743},
abstract = {For blind speech dereverberation, two frameworks are commonly used: on the one hand, the multi-channel linear prediction MCLP framework, and on the other hand, data-dependent beamforming, e.g., the generalized sidelobe canceler GSC framework. The MCLP framework is designed to perform deconvolution and hence has gained increased prominence in blind speech dereverberation. The GSC framework is commonly used for noise reduction, but may be applied for dereverberation as well. In previous work, we have shown that for the noiseless case, MCLP and the GSC yield in theory mathematically equivalent results in terms of dereverberation. In this paper, we assume additional coherent as well as incoherent-noise components and formally analyze and compare both frameworks in terms of dereverberation and noise reduction performance. Both the theoretical analysis and time domain simulation results demonstrate that unlike the GSC, MCLP expectably shows limited performance in terms of noise reduction, while both perform equally well in terms of dereverberation, provided that the GSC blocking matrix achieves complete blocking of the early reverberant-speech component and sufficiently many microphones are available. In case of incomplete blocking, however, the GSC performs inferior to MCLP in terms of dereverberation, as shown in short-time Fourier transform domain simulations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {544–558},
numpages = {15}
}

@article{10.1109/TASLP.2019.2892412,
author = {Li, Xiaofei and Girin, Laurent and Gannot, Sharon and Horaud, Radu},
title = {Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2892412},
doi = {10.1109/TASLP.2019.2892412},
abstract = {This paper addresses the problem of speech separation and enhancement from multichannel convolutive and noisy mixtures, assuming known mixing filters. We propose to perform speech separation and enhancement in the short-time Fourier transform domain using the convolutive transfer function CTF approximation. Compared to time-domain filters, the CTF has much less taps. Consequently, it requires less computational cost and sometimes is more robust against the filter perturbations. We propose three methods: 1 for the multisource case, the multichannel inverse filtering method, i.e., the multiple input/output inverse theorem MINT, is exploited in the CTF domain; 2 a beamforming-like multichannel inverse filtering method applying the single-source MINT and using power minimization, which is suitable whenever the source CTFs are not all known; and 3 a basis pursuit method, where the sources are recovered by minimizing their $ell _1$-norm to impose spectral sparsity, while the $ell _2$-norm fitting cost between microphone signals and mixing model is constrained to be lower than a tolerance. The noise can be reduced by setting this tolerance at the noise power level. Experiments under various acoustic conditions are carried out to evaluate and compare the three proposed methods. Comparison with four baseline methods—beamforming-based, two time-domain inverse filters, and time-domain Lasso—shows the applicability of the proposed methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {645–659},
numpages = {15}
}

@article{10.1109/TASLP.2018.2881536,
author = {Buchris, Yaakov and Amar, Alon and Benesty, Jacob and Cohen, Israel},
title = {Incoherent Synthesis of Sparse Arrays for Frequency-Invariant Beamforming},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2881536},
doi = {10.1109/TASLP.2018.2881536},
abstract = {Frequency-invariant beamformers are used to prevent signal waveform distortions in real world applications like audio, underwater acoustics, and radar. Most of existing methods assume uniform arrays, and only few consider sparse designs, which may lead to higher performance in terms of robustness and directivity factor. We propose an incoherent approach that first determines for each frequency bin a sparse set of sensors positions. Subsequently, by using tools of dimensionality reduction and clustering, these selections are merged together yielding the optimal sensors on a sparse array layout. We present design examples of sparse linear and planar superdirective array designs. We show that the proposed incoherent sparse design obtains superior performance in terms of white noise gain, directivity factor, and computational load compared to a uniform array design and compared to a coherent sparse approach, where the sensors’ locations and the beamformer coefficients are optimized simultaneously for all frequencies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {482–495},
numpages = {14}
}

@article{10.1109/TASLP.2018.2886739,
author = {Gao, Jianqing and Du, Jun and Chen, Enhong},
title = {Mixed-Bandwidth Cross-Channel Speech Recognition via Joint Optimization of DNN-Based Bandwidth Expansion and Acoustic Modeling},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2886739},
doi = {10.1109/TASLP.2018.2886739},
abstract = {Automatic speech recognition ASR systems are often built using scene related speech data due to large variations of transmission channels and sampling rates in different scenarios. In this study, we propose a general framework that establishes a unified model for diversified speech data with different sampling rates and channels. The framework is a joint optimization of deep neural network DNN-based bandwidth expansion and acoustic modeling to exploit a large amount of diversified training data. First, we design two novel DNN architectures to map the acoustic features from narrowband to wideband speech through direct mapping and progressive mapping. The learning targets of the direct mapping DNN DNN-DM are the acoustic features extracted from speech with the largest bandwidth, while the acoustic features from speech with all the other bandwidths are used as input. A progressive stacking network PSN gradually maps the features from the low sampling rates to the highest sampling rate through the design of intermediate target layers via multitask training. Then, in addition to these bandwidth expansion networks, we investigate several joint training strategies for DNN-based acoustic models. Our experiments conducted on three diversified large-scale Mandarin speech datasets with different recording channels and sampling rates 6, 8, and 16 kHz show that the proposed unified model using PSN for bandwidth expansion not only is a more flexible and compact design than conventional multiple acoustic models with each bandwidth for a specific sampling rate, but also yields consistent and significant improvements over bandwidth-dependent models with an average relative word error rate reduction of 6.2%, indicating that the proposed model can fully utilize the diversified cross-channel speech data with multiple bandwidths. Moreover, the proposed methods are verified to be robust on different realistic scenes and can be effectively extended to a long short-term memory framework.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {559–571},
numpages = {13}
}

@article{10.1109/TASLP.2018.2888814,
author = {Deena, Salil and Hasan, Madina and Doulaty, Mortaza and Saz, Oscar and Hain, Thomas},
title = {Recurrent Neural Network Language Model Adaptation for Multi-Genre Broadcast Speech Recognition and Alignment},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2888814},
doi = {10.1109/TASLP.2018.2888814},
abstract = {Recurrent neural network language models RNNLMs generally outperform $n$-gram language models when used in automatic speech recognition ASR. Adapting RNNLMs to new domains is an open problem and current approaches can be categorised as either feature-based or model based. In feature-based adaptation, the input to the RNNLM is augmented with auxiliary features whilst model-based adaptation includes model fine-tuning and the introduction of adaptation layers in the network. In this paper, the properties of both types of adaptation are investigated on multi-genre broadcast speech recognition. Existing techniques for both types of adaptation are reviewed and the proposed techniques for model-based adaptation, namely the linear hidden network adaptation layer and the $K$-component adaptive the RNNLM, are investigated. Moreover, new features derived from the acoustic domain are investigated for the RNNLM adaptation. The contributions of this paper include two hybrid adaptation techniques: the fine-tuning of feature-based RNNLMs and a feature-based adaptation layer. Moreover, the semi-supervised adaptation of RNNLMs using genre information is also proposed. The ASR systems were trained using 700 h of multi-genre broadcast speech. The gains obtained when using the RNNLM adaptation techniques proposed in this paper are consistent when using RNNLMs trained on an in-domain set of 10M words and on a combination of in-domain and out-of-domain sets of 660 M words, with approx. $text{10}{%}$ perplexity and $text{2}{%}$ relative word error rate improvements on a 28.3 h. test set. The best RNNLM adaptation techniques for ASR are also evaluated on a lightly supervised alignment of subtitles task for the same data, where the use of RNNLM adaptation leads to an absolute increase in the F–measure of $text{0.5}{%}$.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {572–582},
numpages = {11}
}

@article{10.1109/TASLP.2018.2885775,
author = {Yang, Guang and He, Haibo and Chen, Qian},
title = {Emotion-Semantic-Enhanced Neural Network},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2885775},
doi = {10.1109/TASLP.2018.2885775},
abstract = {Although sentiment analysis on microblog posts has been studied in depth, sentiment analysis of posts is still challenging because of the limited contextual information that they normally contain. In microblog environments, emoticons are frequently used and they have clear emotional meanings. They are important emotional signals for microblog sentimental analysis. Existing studies typically use emoticons as noisy sentiment labels or similar sentiment indicators to effectively train classifier but overlook their emotional potentiality. We address this issue by constructing an emotional space as a feature representation matrix and projecting emoticons and words into the emotional space based on the semantic composition. To improve the performance of sentimental analysis, we propose a new emotion-semantic-enhanced convolutional neural network ECNN model. ECNN can use emoticon embedding as an emotional space projection operator. By projecting emoticons and words into an emoticon space, it can help identify subjectivity, polarity, and emotion in microblog environments. It is more capable of capturing emotion semantic than other models, so it can improve the sentiment analysis performance. The experimental results show that this model consistently outperforms other models on the dataset of several sentiment tasks. This paper provides insights on the design of ECNN for sentimental analysis in other natural language processing tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {531–543},
numpages = {13}
}

@article{10.1109/TASLP.2018.2882738,
author = {Gelderblom, Femke B. and Tronstad, Tron V. and Viggen, Erlend Magnus},
title = {Subjective Evaluation of a Noise-Reduced Training Target for Deep Neural Network-Based Speech Enhancement},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2882738},
doi = {10.1109/TASLP.2018.2882738},
abstract = {Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this study, we compare two speech enhancement systems based on deep neural networks. The speech intelligibility and quality of both systems were evaluated subjectively by a speech recognition test based on Hagerman sentences and a translation of the ITU-T P.835 recommendation, respectively. Results were compared with the objective measures STOI and POLQA. Neither STOI nor POLQA reliably predicted subjective results. While STOI anticipated improvement, subjective results for both models showed degradation of speech intelligibility. POLQA results were overall hardly affected, while the subjective results showed significant changes in overall quality, both positive and negative, in many of the tests. One of the systems was trained to remove all noise; a strategy that is common in speech enhancement systems found in the literature. The other system was trained to only reduce the noise such that the signal-to-noise ratio increased with 10 dB. The latter system subjectively outperformed the system that attempted to remove noise completely. From this, we conclude that objective evaluation cannot replace subjective evaluation until a measure that reliably predicts intelligibility and quality for deep-neural-network-based systems has been identified. Results further indicate that it may be beneficial to move away from more aggressive noise removal strategies toward noise reduction strategies that cause less speech distortion.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {583–594},
numpages = {12}
}

@article{10.1109/TASLP.2018.2882307,
author = {Moore, Alastair H. and Xue, Wei and Naylor, Patrick A. and Brookes, Mike},
title = {Noise Covariance Matrix Estimation for Rotating Microphone Arrays},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2882307},
doi = {10.1109/TASLP.2018.2882307},
abstract = {The noise covariance matrix computed between the signals from a microphone array is used in the design of spatial filters and beamformers with applications in noise suppression and dereverberation. This paper specifically addresses the problem of estimating the covariance matrix associated with a noise field when the array is rotating during desired source activity, as is common in head-mounted arrays. We propose a parametric model that leads to an analytical expression for the microphone signal covariance as a function of the array orientation and array manifold. An algorithm for estimating the model parameters during noise-only segments is proposed and the performance shown to be improved, rather than degraded, by array rotation. The stored model parameters can then be used to update the covariance matrix to account for the effects of any array rotation that occurs when the desired source is active. The proposed method is evaluated in terms of the Frobenius norm of the error in the estimated covariance matrix and of the noise reduction performance of a minimum variance distortionless response beamformer. In simulation experiments the proposed method achieves 18 dB lower error in the estimated noise covariance matrix than a conventional recursive averaging approach and results in noise reduction which is within 0.05 dB of an oracle beamformer using the ground truth noise covariance matrix.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {519–530},
numpages = {12}
}

@article{10.1109/TASLP.2018.2882913,
author = {Jahromi, Mohsen Zareian and Zahedi, Adel and Jensen, Jesper and Ostergaard, Jan},
title = {Information Loss in the Human Auditory System},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2882913},
doi = {10.1109/TASLP.2018.2882913},
abstract = {From the eardrum to the auditory cortex, where acoustic stimuli are decoded, there are several stages of auditory processing and transmission where information may potentially be lost. In this paper, we aim at quantifying the total information loss in the human auditory system by using information theoretic tools. To do so, we consider a speech communication model, where words are uttered and sent through a noisy channel, and then received and processed by a human listener. We define a notion of information loss that is related to the human word recognition rate. To assess the word recognition rate of humans, we conduct a closed-vocabulary intelligibility test. We derive upper and lower bounds on the information loss. Simulations reveal that the bounds are tight and we observe that the information loss in the human auditory system increases as the signal to noise ratio SNR decreases. Our framework also allows us to study whether humans are optimal in terms of speech perception in a noisy environment. Toward that end, we derive optimal classifiers and compare the human and machine performance in terms of information loss and word recognition rate. We observe a higher information loss and lower word recognition rate for humans compared to the optimal classifiers. In fact, depending on the SNR, the machine classifier may outperform humans by as much as 8 dB. This implies that for the speech-in-stationary-noise setup considered here, the human auditory system is suboptimal for recognizing noisy words.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {472–481},
numpages = {10}
}

@article{10.1109/TASLP.2018.2882731,
author = {Rahulamathavan, Yogachandran and Sutharsini, Kunaraj R and Ray, Indranil Ghosh and Lu, Rongxing and Rajarajan, Muttukrishnan},
title = {Privacy-Preserving IVector-Based Speaker Verification},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2882731},
doi = {10.1109/TASLP.2018.2882731},
abstract = {This paper introduces an efficient algorithm to develop a privacy-preserving voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services i.e., banking. Once enrolment is completed, users can verify themselves using their voice print instead of alphanumeric passwords. Since a voice print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this paper proposes a novel technique based on randomization to carry out voice authentication, which allows the user to enrol and verify their voice in the randomized domain. To achieve this, the iVector-based voice verification technique has been redesigned to work on the randomized domain. The proposed algorithm is validated using a well-known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomization operations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {496–506},
numpages = {11}
}

@article{10.1109/TASLP.2018.2885786,
author = {Luis Valero, Maria and Habets, Emanuel A. P.},
title = {Low-Complexity Multi-Microphone Acoustic Echo Control in the Short-Time Fourier Transform Domain},
year = {2019},
issue_date = {March 2019},
publisher = {IEEE Press},
volume = {27},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2885786},
doi = {10.1109/TASLP.2018.2885786},
abstract = {Many modern communication and smart devices are equipped with several microphones, in addition to one or more loudspeakers. Each microphone not only acquires sounds produced in the near-end room, i.e., desired near-end speech, background noise, and other interferences, but also a far-end signal that is reproduced by the loudspeakers. This particular type of acoustic coupling, commonly denoted as acoustic echo, can be reduced in a distortionless manner by employing multi-microphone acoustic echo cancellation MM-AEC techniques. However, under noisy conditions, the performance of AEC is limited by the echo-to-noise ratio, and additional echo reduction is needed. Further, to ensure high-quality end-to-end communication in noisy environments, background noise has to be reduced as well. To achieve the latter, multi-microphone speech enhancement techniques, such as beamforming BF, are often used as they are capable of reducing undesired signal components while causing little distortion to the desired near-end speech. In spite of its high computational cost, the most effective solution to reduce acoustic echoes and background noise is to cascade MM-AEC and BF. In this work, a low-complexity multi-microphone echo controller is introduced, which not only combines low-complexity MM-AEC with BF, but also integrates residual echo reduction into the beamformer design.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {595–609},
numpages = {15}
}

@article{10.1109/TASLP.2018.2879399,
author = {Wu, Yiming and Li, Wei},
title = {Automatic Audio Chord Recognition With MIDI-Trained Deep Feature and BLSTM-CRF Sequence Decoding Model},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2879399},
doi = {10.1109/TASLP.2018.2879399},
abstract = {With the advances of machine learning technologies, data-driven feature extraction and sequence modeling approaches are being widely explored for automatic chord recognition tasks. Currently, there is a bottleneck in the amount of enough annotated data for training robust acoustic models, as hand-annotating time-synchronized chord labels requires professional musical skills and considerable labor. To cope with this limitation, in this paper, we propose a convolutional neural network CNN based deep feature extractor, which is trained on a large set of time, synchronized musical instrument digital interface audio data pairs and can robustly estimate pitch class activations of real-world music audio recordings. The CNN feature extractor plus a bidirectional long short-term memory conditional random field decoding model forms the proposed hybrid system for automatic chord recognition. Experiments show that the proposed model is compatible for both regular major/minor triad chord classification and larger vocabulary chord recognition, and outperforms other state-of-the-art chord recognition systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {355–366},
numpages = {12}
}

@article{10.1109/TASLP.2018.2881912,
author = {Wang, Zhong-Qiu and Wang, DeLiang},
title = {Combining Spectral and Spatial Features for Deep Learning Based Blind Speaker Separation},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2881912},
doi = {10.1109/TASLP.2018.2881912},
abstract = {This study tightly integrates complementary spectral and spatial features for deep learning based multi-channel speaker separation in reverberant environments. The key idea is to localize individual speakers so that an enhancement network can be trained on spatial as well as spectral features to extract the speaker from an estimated direction and with specific spectral structures. The spatial and spectral features are designed in a way such that the trained models are blind to the number of microphones and microphone geometry. To determine the direction of the speaker of interest, we identify time-frequency T-F units dominated by that speaker and only use them for direction estimation. The T-F unit level speaker dominance is determined by a two-channel chimera++ network, which combines deep clustering and permutation invariant training at the objective function level, and integrates spectral and interchannel phase patterns at the input feature level. In addition, T-F masking based beamforming is tightly integrated in the system by leveraging the magnitudes and phases produced by beamforming. Strong separation performance has been observed on reverberant talker-independent speaker separation, which separates reverberant speaker mixtures based on a random number of microphones arranged in arbitrary linear-array geometry.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {457–468},
numpages = {12}
}

@article{10.1109/TASLP.2018.2877909,
author = {Kolbaek, Morten and Tan, Zheng-Hua and Jensen, Jesper},
title = {On the Relationship Between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2877909},
doi = {10.1109/TASLP.2018.2877909},
abstract = {The majority of deep neural network DNN based speech enhancement algorithms rely on the mean-square error MSE criterion of short-time spectral amplitudes STSA, which has no apparent link to human perception, e.g., speech intelligibility. Short-time objective intelligibility STOI, a popular state-of-the-art speech intelligibility estimator, on the other hand, relies on linear correlation of speech temporal envelopes. This raises the question if a DNN training criterion based on envelope linear correlation ELC can lead to improved speech intelligibility performance of DNN-based speech enhancement algorithms compared to algorithms based on the STSA–MSE criterion. In this paper, we derive that, under certain general conditions, the STSA–MSE and ELC criteria are practically equivalent, and we provide empirical data to support our theoretical results. Furthermore, our experimental findings suggest that the standard STSA minimum-MSE estimator is near optimal, if the objective is to enhance noisy speech in a manner, which is optimal with respect to the STOI speech intelligibility estimator.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {283–295},
numpages = {13}
}

@article{10.1109/TASLP.2018.2879855,
author = {Imoto, Keisuke and Ono, Nobutaka},
title = {Acoustic Topic Model for Scene Analysis With Intermittently Missing Observations},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2879855},
doi = {10.1109/TASLP.2018.2879855},
abstract = {We propose a sophisticated method of acoustic scene analysis with intermittently missing observations, which analyzes acoustic scenes and restores missing observations simultaneously on the basis of the temporal correlation between acoustic words. One effective strategy for analyzing acoustic scenes is to characterize them as a combination of acoustic words. An acoustic topic model ATM is one of the techniques, which models the process generating multiple acoustic words. Here, an acoustic word corresponds to a sound category, while it has a homogenous time duration and is defined time frame by time frame. In the ATM, it is assumed that all acoustic words are observed, and therefore, it cannot be applied if any acoustic observations are missing. However, acoustic observations may sometimes be missing because of poor recording conditions, transmission loss, or privacy reasons. In the proposed method, focusing on the fact that acoustic words are temporally correlated, we consider the transition of acoustic words in two ways: First, by modeling the temporal transition of acoustic words directly using a Markov process and finally, by modeling the temporal transition of hidden states that generate acoustic words using a hidden Markov model. We then incorporate each transition model in a process generating acoustic words based on the ATM. The proposed method allows us to analyze acoustic scenes from acoustic words by restoring missing acoustic words. In our experiments, the proposed method exhibited a classification accuracy of acoustic scenes close to that for the case of no missing observations even when 50% of the observations were missing. Moreover, the model considering the hidden-state transition can classify acoustic scenes more accurately than the model considering the acoustic word transition directly.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {367–382},
numpages = {16}
}

@article{10.1109/TASLP.2018.2868428,
author = {Zhang, Yichi and Pardo, Bryan and Duan, Zhiyao},
title = {Siamese Style Convolutional Neural Networks for Sound Search by Vocal Imitation},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2868428},
doi = {10.1109/TASLP.2018.2868428},
abstract = {Conventional methods for finding audio in databases typically search text labels, rather than the audio itself. This can be problematic as labels may be missing, irrelevant to the audio content, or not known by users. Query by vocal imitation lets users query using vocal imitations instead. To do so, appropriate audio feature representations and effective similarity measures of imitations and original sounds must be developed. In this paper, we build upon our preliminary work to propose Siamese style convolutional neural networks to learn feature representations and similarity measures in a unified end-to-end training framework. Our Siamese architecture uses two convolutional neural networks to extract features, one from vocal imitations and the other from original sounds. The encoded features are then concatenated and fed into a fully connected network to estimate their similarity. We propose two versions of the system: IMINET is symmetric where the two encoders have an identical structure and are trained from scratch, while TL-IMINET is asymmetric and adopts the transfer learning idea by pretraining the two encoders from other relevant tasks: spoken language recognition for the imitation encoder and environmental sound classification for the original sound encoder. Experimental results show that both versions of the proposed system outperform a state-of-the-art system for sound search by vocal imitation, and the performance can be further improved when they are fused with the state of the art system. Results also show that transfer learning significantly improves the retrieval performance. This paper also provides insights to the proposed networks by visualizing and sonifying input patterns that maximize the activation of certain neurons in different layers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {429–441},
numpages = {13}
}

@article{10.1109/TASLP.2018.2878618,
author = {Koutrouvelis, Andreas I. and Hendriks, Richard C. and Heusdens, Richard and Jensen, Jesper},
title = {A Convex Approximation of the Relaxed Binaural Beamforming Optimization Problem},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2878618},
doi = {10.1109/TASLP.2018.2878618},
abstract = {The recently proposed relaxed binaural beamforming RBB optimization problem provides a flexible tradeoff between noise suppression and binaural-cue preservation of the sound sources in the acoustic scene. It minimizes the output noise power, under the constraints, which guarantee that the target remains unchanged after processing and the binaural-cue distortions of the acoustic sources will be less than a user-defined threshold. However, the RBB problem is a computationally demanding non convex optimization problem. The only existing suboptimal method which approximately solves the RBB is a successive convex optimization SCO method which, typically, requires to solve multiple convex optimization problems per frequency bin, in order to converge. Convergence is achieved when all constraints of the RBB optimization problem are satisfied. In this paper, we propose a semidefinite convex relaxation SDCR of the RBB optimization problem. The proposed suboptimal SDCR method solves a single convex optimization problem per frequency bin, resulting in a much lower computational complexity than the SCO method. Unlike the SCO method, the SDCR method does not guarantee user-controlled upper-bounded binaural-cue distortions. To tackle this problem, we also propose a suboptimal hybrid method that combines the SDCR and SCO methods. Instrumental measures combined with a listening test show that the SDCR and hybrid methods achieve significantly lower computational complexity than the SCO method, and in most cases better tradeoff between predicted intelligibility and binaural-cue preservation than the SCO method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {321–331},
numpages = {11}
}

@article{10.1109/TASLP.2018.2880607,
author = {Xiao, Ke and Wang, Supin and Wan, Mingxi and Wu, Liang},
title = {Reconstruction of Mandarin Electrolaryngeal Fricatives With Hybrid Noise Source},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2880607},
doi = {10.1109/TASLP.2018.2880607},
abstract = {The Mandarin electrolaryngeal EL speech is suffering from severe fricative confusion due to improper EL source in EL speech production and abnormal physiological structure of vocal tract in the laryngectomized condition. To reduce the fricative confusions, this paper proposes a hybrid noise source by combining the typical natural fricative sources and compensation sources that consider the acoustic defects in the frequency domain caused by the truncated vocal tract and abnormal source location in EL speech production. All parameters of the model are fricative-specific and the parameters of the compensation sources are determined by analyzing the vocal tract transfer functions before and after the laryngectomy. All five Mandarin fricatives are produced by laryngectomized subjects with an experimental EL system loading the hybrid noise source and the wideband noise source. The acoustic and perceptual features of these reconstructed EL fricatives are analyzed and evaluated by comparing with the conventional EL fricatives and normal fricatives. The results indicate that the hybrid noise source successfully improves the acoustic properties of the EL fricatives by forming better spectral shapes, raising the frequencies of average energy concentration, and producing better spectral skewness and kurtosis. Finally, due to these improvements of acoustic properties, the hybrid noise sources achieve much larger intelligibility for EL fricatives than the wideband noise source and the conventional EL source. Thus, the hybrid noise source is an effective, feasible, and promising method of reducing the severe fricative confusions and improving the intelligibility of EL speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {383–391},
numpages = {9}
}

@article{10.1109/TASLP.2018.2881925,
author = {Feng, Fangchen and Kowalski, Matthieu},
title = {Underdetermined Reverberant Blind Source Separation: Sparse Approaches for Multiplicative and Convolutive Narrowband Approximation},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2881925},
doi = {10.1109/TASLP.2018.2881925},
abstract = {We consider the problem of blind source separation for underdetermined convolutive mixtures. Based on the multiplicative narrowband approximation in the time-frequency domain with the help of the short-time-Fourier-transform STFT and the sparse representation of the source signals, we formulate the separation problem in an optimization framework. This framework is then generalized based on the recently investigated convolutive narrowband approximation and the statistics of the room impulse response. Algorithms with convergence proof are then employed to solve the proposed optimization problems. The evaluation of the proposed frameworks and algorithms for synthesized and live recorded mixtures are illustrated. The proposed approaches are also tested for mixtures with input noise. Numerical evaluations show the advantages of the proposed methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {442–456},
numpages = {15}
}

@article{10.1109/TASLP.2018.2878616,
author = {Pishdadian, Fatemeh and Pardo, Bryan},
title = {Multi-Resolution Common Fate Transform},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2878616},
doi = {10.1109/TASLP.2018.2878616},
abstract = {The multi-resolution common fate transform MCFT is an audio signal representation useful for representing mixtures of multiple audio signals that overlap in both time and frequency. The MCFT combines the invertibility of a state-of-the-art representation, the common fate transform CFT, and the multi-resolution property of the cortical stage output of an auditory model. Since the MCFT is computed based on a fully invertible complex time–frequency representation, separation of audio sources with high time–frequency overlap may be performed directly in the MCFT domain, where there is less overlap between sources than in the time–frequency domain. The MCFT circumvents the resolution issue of the CFT by using a multi-resolution two-dimensional 2D filter bank instead of fixed-size 2D windows. This enables higher quality separation without the need to hand-tune the window size to the specific case. In this work, we describe the MCFT, discuss the properties of the MCFT with the aid of illustrative examples, and provide definitions and objective measures for two desirable representation properties: separability of source signals and clusterability of components of each signal. The utility of the MCFT for source separation is illustrated by performing ideal masking on a comprehensive dataset of audio mixtures of musical tones played in unison, including audio samples from a wide pitch range and a variety of instruments/playing techniques. Results show that the ideal masks made in the MCFT domain yield better separability than those made in commonly used time–frequency signal representations as well as the CFT. The use of the MCFT also results in more reliable clusterability than the CFT in most cases.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {342–354},
numpages = {13}
}

@article{10.1109/TASLP.2018.2878384,
author = {Hansen, Martin Weiss and Jensen, Jesper Rindom and Christensen, Mads Graesboll},
title = {Estimation of Fundamental Frequencies in Stereophonic Music Mixtures},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2878384},
doi = {10.1109/TASLP.2018.2878384},
abstract = {In this paper, a method for multi-pitch estimation of stereophonic mixtures of harmonic signals, e.g., instrument recordings, is presented. The proposed method is based on a signal model that includes the panning parameters of the sources in a stereophonic mixture, such as those applied artificially in a recording studio. If the sources in a mixture have different panning parameters, this diversity can be used to simplify the pitch estimation problem. The mixing parameters of the sources might be shared, resulting in a multi-pitch estimation problem, which is solved using an approach based on an expectation–maximization algorithm for Gaussian sources, where the fundamental frequencies and model orders are estimated jointly. The fundamental frequencies may be related, resulting in overlapping harmonics, complicating the estimation of the parameters. A codebook of harmonic amplitude vectors is trained on recordings of instruments playing single notes, and used when estimating the amplitudes of the mixture components. The proposed method is evaluated using stereophonic mixtures of instrument recordings and is compared to state-of-the-art transcription and multi-pitch estimation methods. Experiments show an increase in performance when knowledge about the panning parameters is taken into account. The proposed method provides a full parameterization of the components of the observed signal. Possible applications include instrument tuning, audio editing tools, modification of harmonic mixture components, and audio effects.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {296–310},
numpages = {15}
}

@article{10.1109/TASLP.2018.2878381,
author = {Bao, Junwei and Tang, Duyu and Duan, Nan and Yan, Zhao and Zhou, Ming and Zhao, Tiejun},
title = {Text Generation From Tables},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2878381},
doi = {10.1109/TASLP.2018.2878381},
abstract = {This paper proposes a neural generative model, namely Table2Seq, to generate a natural language sentence based on a table. Specifically, the model maps a table to continuous vectors and then generates a natural language sentence by leveraging the semantics of a table. Since rare words, e.g., entities and values, usually appear in a table, we develop a flexible copying mechanism that selectively replicates contents from the table to the output sequence. We conduct extensive experiments to demonstrate the effectiveness of our Table2Seq model and the utility of the designed copying mechanism. On the WIKIBIO and SIMPLEQUESTIONS datasets, the Table2Seq model improves the state-of-the-art results from 34.70 to 40.26 and from 33.32 to 39.12 in terms of BLEU-4 scores, respectively. Moreover, we construct an open-domain dataset WIKITABLETEXT that includes 13 318 descriptive sentences for 4962 tables. Our Table2Seq model achieves a BLEU

license_type: permissive

branch_name: refs/heads/main

extension: bib