Online Monaural Speech Enhancement Using Delayed Subband LSTM

This paper proposes a delayed subband LSTM network for online monaural (single-channel) speech enhancement. The proposed method is developed in the short time Fourier transform (STFT) domain. Online processing requires frame-by-frame signal reception and processing. A paramount feature of the proposed method is that the same LSTM is used across frequencies, which drastically reduces the number of network parameters, the amount of training data and the computational burden. Training is performed in a subband manner: the input consists of one frequency, together with a few context frequencies. The network learns a speech-to-noise discriminative function relying on the signal stationarity and on the local spectral pattern, based on which it predicts a clean-speech mask at each frequency. To exploit future information, i.e. look-ahead, we propose an output-delayed subband architecture, which allows the unidirectional forward network to process a few future frames in addition to the current frame. We leverage the proposed method to participate to the DNS real-time speech enhancement challenge. Experiments with the DNS dataset show that the proposed method achieves better performance-measuring scores than the DNS baseline method, which learns the full-band spectra using a gated recurrent unit network.Comment: Paper submitted to Interspeech 202

Evaluation of the Importance of Time-Frequency Contributions to Speech Intelligibility in Noise

Recent studies on binary masking techniques make the assumption that each time-frequency (T-F) unit contributes an equal amount to the overall intelligibility of speech. The present study demonstrated that the importance of each T-F unit to speech intelligibility varies in accordance with speech content. Specifically, T-F units are categorized into two classes, speech-present T-F units and speech-absent T-F units. Results indicate that the importance of each speech-present T-F unit to speech intelligibility is highly related to the loudness of its target component, while the importance of each speech-absent T-F unit varies according to the loudness of its masker component. Two types of mask errors are also considered, which include miss and false alarm errors. Consistent with previous work, false alarm errors are shown to be more harmful to speech intelligibility than miss errors when the mixture signal-to-noise ratio (SNR) is below 0 dB. However, the relative importance between the two types of error is conditioned on the SNR level of the input speech signal. Based on these observations, a mask-based objective measure, the loudness weighted hit-false, is proposed for predicting speech intelligibility. The proposed objective measure shows significantly higher correlation with intelligibility compared to two existing mask-based objective measures

Sub-Band Knowledge Distillation Framework for Speech Enhancement

In single-channel speech enhancement, methods based on full-band spectral features have been widely studied. However, only a few methods pay attention to non-full-band spectral features. In this paper, we explore a knowledge distillation framework based on sub-band spectral mapping for single-channel speech enhancement. Specifically, we divide the full frequency band into multiple sub-bands and pre-train an elite-level sub-band enhancement model (teacher model) for each sub-band. These teacher models are dedicated to processing their own sub-bands. Next, under the teacher models' guidance, we train a general sub-band enhancement model (student model) that works for all sub-bands. Without increasing the number of model parameters and computational complexity, the student model's performance is further improved. To evaluate our proposed method, we conducted a large number of experiments on an open-source data set. The final experimental results show that the guidance from the elite-level teacher models dramatically improves the student model's performance, which exceeds the full-band model by employing fewer parameters.Comment: Published in Interspeech 202

Psychophysical and electrophysiological investigations into the mechanisms supporting everyday communication

Thesis (Ph.D.)--Boston UniversityHumans solve the so-called "cocktail party problem" with relative ease, and are generally able to selectively direct their attention to process and recall acoustic information from one sound source in the presence of other irrelevant stimuli that are competing for cognitive resources. This ability depends on a variety of factors, including volitional control of selective attention, the ability to store information in memory for recall at a later time, and the ability to integrate information across multiple sensory modalities. Here, psychophysical and electroencephalography (EEG) experiments were conducted to study these three factors. The effects of selective attention on cortical and subcortical structures were examined using EEG recorded during a dichotic listening task. Cortical potentials showed robust effects of attention (demonstrated by the ability to classify responses to attended and ignored speech based on short segments of EEG responses); however, potentials originating in the brainstem did not, even though stimuli were engineered to maximize the separability of the neural representation of the competing sources in the auditory periphery and thus the possibility of seeing attention-specific modulation of subcortical responses. In another study, the relationship between object formation and memory processing was explored in a psychophysical experiment examining how sequences of nonverbal auditory stimuli are stored and recalled from short-term memory. The results of this study support the notion that auditory short-term memory, like visual short-term memory, can be explained in terms of object formation. In particular, short-term memory performance is affected by stream formation and the perceptual costs involved in switching attention between multiple streams. Finally, effects of audiovisual integration were studied in a psychophysical experiment using complex speech-like stimuli (zebra finch songs). Results show visual cues improve performance differently depending on whether target identification is limited by energetic masking or whether it is limited by object formation difficulties and uncertainty about when a target occurs. Together, these studies support the idea that everyday communication depends on an interplay of many mechanisms including attention, memory, and multisensory integration, each of which is influenced by perceptual organization

Denoising Deep Neural Networks Based Voice Activity Detection

Recently, the deep-belief-networks (DBN) based voice activity detection (VAD) has been proposed. It is powerful in fusing the advantages of multiple features, and achieves the state-of-the-art performance. However, the deep layers of the DBN-based VAD do not show an apparent superiority to the shallower layers. In this paper, we propose a denoising-deep-neural-network (DDNN) based VAD to address the aforementioned problem. Specifically, we pre-train a deep neural network in a special unsupervised denoising greedy layer-wise mode, and then fine-tune the whole network in a supervised way by the common back-propagation algorithm. In the pre-training phase, we take the noisy speech signals as the visible layer and try to extract a new feature that minimizes the reconstruction cross-entropy loss between the noisy speech signals and its corresponding clean speech signals. Experimental results show that the proposed DDNN-based VAD not only outperforms the DBN-based VAD but also shows an apparent performance improvement of the deep layers over shallower layers.Comment: This paper has been accepted by IEEE ICASSP-2013, and will be published online after May, 201

Deep neural network techniques for monaural speech enhancement: state of the art analysis

Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in these domains in task such as machine translation and image generation. Due to their success, these data driven techniques have been applied in audio domain. More specifically, DNN models have been applied in speech enhancement domain to achieve denosing, dereverberation and multi-speaker separation in monaural speech enhancement. In this paper, we review some dominant DNN techniques being employed to achieve speech separation. The review looks at the whole pipeline of speech enhancement from feature extraction, how DNN based tools are modelling both global and local features of speech and model training (supervised and unsupervised). We also review the use of speech-enhancement pre-trained models to boost speech enhancement process. The review is geared towards covering the dominant trends with regards to DNN application in speech enhancement in speech obtained via a single speaker.Comment: conferenc

Binaural Source Separation with Convolutional Neural Networks

This work is a study on source separation techniques for binaural music mixtures. The chosen framework uses a Convolutional Neural Network (CNN) to estimate time-frequency soft masks. This masks are used to extract the different sources from the original two-channel mixture signal. Its baseline single-channel architecture performed state-of-the-art results on monaural music mixtures under low-latency conditions. It has been extended to perform separation in two-channel signals, being the first two-channel CNN joint estimation architecture. This means that filters are learned for each source by taking in account both channels information. Furthermore, a specific binaural condition is included during training stage. It uses Interaural Level Difference (ILD) information to improve spatial images of extracted sources. Concurrently, we present a novel tool to create binaural scenes for testing purposes. Multiple binaural scenes are rendered from a music dataset of four instruments (voice, drums, bass and others). The CNN framework have been tested for these binaural scenes and compared with monaural and stereo results. The system showed a great amount of adaptability and good separation results in all the scenarios. These results are used to evaluate spatial information impact on separation performance

Binaural scene analysis : localization, detection and recognition of speakers in complex acoustic scenes

The human auditory system has the striking ability to robustly localize and recognize a specific target source in complex acoustic environments while ignoring interfering sources. Surprisingly, this remarkable capability, which is referred to as auditory scene analysis, is achieved by only analyzing the waveforms reaching the two ears. Computers, however, are presently not able to compete with the performance achieved by the human auditory system, even in the restricted paradigm of confronting a computer algorithm based on binaural signals with a highly constrained version of auditory scene analysis, such as localizing a sound source in a reverberant environment or recognizing a speaker in the presence of interfering noise. In particular, the problem of focusing on an individual speech source in the presence of competing speakers, termed the cocktail party problem, has been proven to be extremely challenging for computer algorithms. The primary objective of this thesis is the development of a binaural scene analyzer that is able to jointly localize, detect and recognize multiple speech sources in the presence of reverberation and interfering noise. The processing of the proposed system is divided into three main stages: localization stage, detection of speech sources, and recognition of speaker identities. The only information that is assumed to be known a priori is the number of target speech sources that are present in the acoustic mixture. Furthermore, the aim of this work is to reduce the performance gap between humans and machines by improving the performance of the individual building blocks of the binaural scene analyzer. First, a binaural front-end inspired by auditory processing is designed to robustly determine the azimuth of multiple, simultaneously active sound sources in the presence of reverberation. The localization model builds on the supervised learning of azimuthdependent binaural cues, namely interaural time and level differences. Multi-conditional training is performed to incorporate the uncertainty of these binaural cues resulting from reverberation and the presence of competing sound sources. Second, a speech detection module that exploits the distinct spectral characteristics of speech and noise signals is developed to automatically select azimuthal positions that are likely to correspond to speech sources. Due to the established link between the localization stage and the recognition stage, which is realized by the speech detection module, the proposed binaural scene analyzer is able to selectively focus on a predefined number of speech sources that are positioned at unknown spatial locations, while ignoring interfering noise sources emerging from other spatial directions. Third, the speaker identities of all detected speech sources are recognized in the final stage of the model. To reduce the impact of environmental noise on the speaker recognition performance, a missing data classifier is combined with the adaptation of speaker models using a universal background model. This combination is particularly beneficial in nonstationary background noise