Kevin Wilson
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Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
Zhong-Qiu Wang
Desh Raj
Shinji Watanabe
Zhuo Chen
IEEE SLT 2021
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This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks for separation use an advanced convolutional architecture trained with a novel stabilized signal-to-noise ratio loss function. For beamforming, we explore multiple ways of computing time-varying covariance matrices, including factorizing the spatial covariance into a time-varying amplitude component and a time-invariant spatial component, as well as using block-based techniques. In addition, we introduce a multi-frame beamforming method which improves the results significantly by adding contextual frames to the beamforming formulations. We extensively evaluate and analyze the effects of window size, block size, and multi-frame context size for these methods. Our best method utilizes a sequence of three neural separation and multiframe time-invariant spatial beamforming stages, and demonstrates an average improvement of 2.75 dB in scale-invariant signal-to-noise ratio and 14.2% absolute reduction in a comparative speech recognition metric across four challenging reverberant speech enhancement and separation tasks. We also use our three-speaker separation model to separate real recordings in the LibriCSS evaluation set into non-overlapping tracks, and achieve a better word error rate as compared to a baseline mask based beamformer.
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Unsupervised Speech Separation Using Mixtures of Mixtures
ICML 2020 Workshop on Self-Supervision for Audio and Speech
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Supervised approaches to single-channel speech separation rely on synthetic mixtures, so that the individual sources can be used as targets. Good performance depends upon how well the synthetic mixture data match real mixtures. However, matching synthetic data to the acoustic properties and distribution of sounds in a target domain can be challenging. Instead, we propose an unsupervised method that requires only singlechannel acoustic mixtures, without ground-truth source signals. In this method, existing mixtures are mixed together to form a mixture of mixtures, which the model separates into latent sources. We propose a novel loss that allows the latent sources
to be remixed to approximate the original mixtures. Experiments show that this method can achieve competitive performance on speech separation compared to supervised methods. In a semisupervised learning setting, our method enables domain adaptation by incorporating unsupervised mixtures from a matched domain. In particular, we demonstrate that significant improvement to reverberant speech separation performance can be achieved by incorporating reverberant mixtures.
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In recent years, rapid progress has been made on the problem of single-channel sound separation using supervised training of deep neural networks. In such supervised approaches, a model is trained to predict the component sources from synthetic mixtures created by adding up isolated ground-truth sources. Reliance on this synthetic training data is problematic because good performance depends upon the degree of match between the training data and real-world audio, especially in terms of the acoustic conditions and distribution of sources. The acoustic properties can be challenging to accurately simulate, and the distribution of sound types may be hard to replicate. In this paper, we propose a completely unsupervised method, mixture invariant training (MixIT), that requires only single-channel acoustic mixtures. In MixIT, training examples are constructed by mixing together existing mixtures, and the model separates them into a variable number of latent sources, such that the separated sources can be remixed to approximate the original mixtures. We show that MixIT can achieve competitive performance compared to supervised methods on speech separation. Using MixIT in a semi-supervised learning setting enables unsupervised domain adaptation and learning from large amounts of real world data without ground-truth source waveforms. In particular, we significantly improve reverberant speech separation performance by incorporating reverberant mixtures, train a speech enhancement system from noisy mixtures, and improve universal sound separation by incorporating a large amount of in-the-wild data.
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VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition
Mert Saglam
Alan Chiao
Renjie Liu
Wei Li
Jason Pelecanos
Marily Nika
Interspeech 2020(2020) (to appear)
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We introduce VoiceFilter-Lite, a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speech recognition system. Delivering such a model presents numerous challenges: It should improve the performance when the input signal consists of overlapped speech, and must not hurt the speech recognition performance under all other acoustic conditions. Besides, this model must be tiny, fast, and perform inference in a streaming fashion, in order to have minimal impact on CPU, memory, battery and latency. We propose novel techniques to meet these multi-faceted requirements, including using a new asymmetric loss, and adopting adaptive runtime suppression strength. We also show that such a model can be quantized as a 8-bit integer model and run in realtime.
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Universal Sound Separation
Ilya Kavalerov
Jonathan Le Roux
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)(2019)
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Recent deep learning approaches have achieved impressive performance on speech enhancement and separation tasks. However, these approaches have not been investigated for separating mixtures of arbitrary sounds of different types, a task we refer to as universal sound separation, and it is unknown whether performance on speech tasks carries over to non-speech tasks. To study this question, we develop a universal dataset of mixtures containing arbitrary sounds, and use it to investigate the space of mask-based separation architectures, varying both the overall network architecture and the framewise analysis-synthesis basis for signal transformations. These network architectures include convolutional long short-term memory networks and time-dilated convolution stacks inspired by the recent success of time-domain enhancement networks like ConvTasNet. For the latter architecture, we also propose novel modifications that further improve separation performance. In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet, and for both of these bases, we examine the effect of window size. In particular, for STFTs, we find that longer windows (25-50 ms) work best for speech/non-speech separation, while shorter windows (2.5 ms) work best for arbitrary sounds. For learnable bases, shorter windows (2.5 ms) work best on all tasks. Surprisingly, for universal sound separation, STFTs outperform learnable bases. Our best methods produce an improvement in scale-invariant signal-to-distortion ratio of over 13 dB for speech/non-speech separation and close to 10 dB for universal sound separation.
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Differentiable Consistency Constraints for Improved Deep Speech Enhancement
Jeremy Thorpe
Michael Chinen
IEEE International Conference on Acoustics, Speech, and Signal Processing(2019)
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In recent years, deep networks have led to dramatic improvements in speech enhancement by framing it as a data-driven pattern recognition problem. In many modern enhancement systems, large amounts of data are used to train a deep network to estimate masks for complex-valued short-time Fourier transforms (STFTs) to suppress noise and preserve speech. However, current masking approaches often neglect two important constraints: STFT consistency and mixture consistency. Without STFT consistency, the system’s output is not necessarily the STFT of a time-domain signal, and without mixture consistency, the sum of the estimated sources does not necessarily equal the input mixture. Furthermore, the only previous approaches that apply mixture consistency use real-valued masks; mixture consistency has been ignored for complex-valued masks. In this paper, we show that STFT consistency and mixture consistency can be jointly imposed by adding simple differentiable projection layers to the enhancement network. These layers are compatible with real or complex-valued masks. Using both of these constraints with complex-valued masks provides a 0.7 dB increase in scale-invariant signal-to-distortion ratio (SI-SDR) on a large dataset of speech corrupted by a wide variety of nonstationary noise across a range of input SNRs.
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VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
Prashant Sridhar
Ye Jia
ICASSP 2019(2018)
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In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals.
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Looking to Listen at the Cocktail Party: Audio-visual Speech Separation
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2018)
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We present a model for isolating and enhancing speech of desired speakers in a video. The input is a video with one or more people speaking, where the speech of interest is interfered by other speakers and/or background noise. We leverage both audio and visual features for this task, which are fed into a joint audio-visual source separation model we designed and trained using thousands of hours of video segments with clean speech from our new dataset, AVSpeech-90K. We present results for various real, practical scenarios involving heated debates and interviews, noisy bars and screaming children, only requiring users to specify the face of the person in the video whose speech they would like to isolate.
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AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies
Liat Kaver
Radhika Marvin
Nathan Christopher Reale
Loretta Guarino Reid
Proceedings of Interspeech, 2018
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Speech activity detection (or endpointing) is an important processing step for applications such as speech recognition, language identification and speaker diarization. Both audio- and vision-based approaches have been used for this task in various settings and with multiple variations tailored toward applications. Unfortunately, much of the prior work reports results in synthetic settings, on task-specific datasets, or on datasets that are not openly available. This makes it difficult to compare approaches in similar settings and to understand their strengths and weaknesses. In this paper, we describe a new dataset of densely labeled speech activity in YouTube video clips, which has been designed to address these issues and will be released publicly. The dataset labels go beyond speech alone, annotating three specific speech activity situations: clean speech, speech and music co-occurring, and speech and noise co-occurring. These classes will enable further analysis of model performance in the presence of noise. We report benchmark performance numbers on this dataset using state-of-the-art audio and vision models.
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EXPLORING TRADEOFFS IN MODELS FOR LOW-LATENCY SPEECH ENHANCEMENT
Jeremy Thorpe
Michael Chinen
Proceedings of the 16th International Workshop on Acoustic Signal Enhancement(2018)
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We explore a variety of configurations of neural networks for one- and
two-channel spectrogram-mask-based speech enhancement. Our best model improves on
state-of-the-art performance on the CHiME2 speech enhancement task.
We examine trade-offs among non-causal lookahead, compute work, and parameter count versus enhancement performance and find that zero-lookahead models can achieve, on average, only 0.5 dB worse performance than our best bidirectional model. Further, we find that 200 milliseconds of lookahead is sufficient to achieve performance within about 0.2 dB from our best bidirectional model.
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