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Register a new userMonaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network
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Qiquan Zhang
Publication date
2019
fields
Electronic Engineering
and research's language is
English
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Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by the ability to capture the long-term effective history information. The recurrent neural networks (RNNs), e.g., long short-term memory (LSTM) model, are able to capture the long-term temporal dependencies, but come with the issues of the high latency and the complexity of training.To address these issues, the temporal convolutional network (TCN) was proposed to replace the RNNs in various sequence modeling tasks. In this paper we propose a novel TCN model that employs multi-branch structure, called multi-branch TCN (MB-TCN), for monaural speech enhancement.The MB-TCN exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity.Inspired by the TCN, the MB-TCN model incorporates one dimensional causal dilated CNN and residual learning to expand receptive fields for capturing long-term temporal contextual information.Our extensive experimental investigation suggests that the MB-TCNs outperform the residual long short-term memory networks (ResLSTMs), temporal convolutional networks (TCNs), and the CNN networks that employ dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-TCN model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics.
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Multi-stage learning is an effective technique to invoke multiple deep-learning modules sequentially. This paper applies multi-stage learning to speech enhancement by using a multi-stage structure, where each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A fusion block is inserted at the input of later stages to re-inject original information. The resulting multi-stage speech enhancement system, in short, multi-stage SA-TCN, is compared with state-of-the-art deep-learning speech enhancement methods using the LibriSpeech and VCTK data sets. The multi-stage SA-TCN systems hyper-parameters are fine-tuned, and the impact of the SA block, the fusion block and the number of stages are determined. The use of a multi-stage SA-TCN system as a front-end for automatic speech recognition systems is investigated as well. It is shown that the multi-stage SA-TCN systems perform well relative to other state-of-the-art systems in terms of speech enhancement and speech recognition scores.
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