Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation

• URL: http://arxiv.org/abs/2010.01703v2
• Date: Mon, 24 May 2021 15:00:30 GMT
• Title: Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation
• Authors: Zhong-Qiu Wang and Peidong Wang and DeLiang Wang
• Abstract summary: We propose multi-microphone complex spectral mapping for speaker separation in reverberant conditions. Our system is trained on simulated room impulse responses based on a fixed number of microphones arranged in a given geometry. State-of-the-art separation performance is obtained on the simulated two-talker SMS-WSJ corpus and the real-recorded LibriCSS dataset.
• Score: 79.63545132515188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditions. We aim at both speaker separation and dereverberation. Our study first investigates offline utterance-wise speaker separation and then extends to block-online continuous speech separation (CSS). Assuming a fixed array geometry between training and testing, we train deep neural networks (DNN) to predict the real and imaginary (RI) components of target speech at a reference microphone from the RI components of multiple microphones. We then integrate multi-microphone complex spectral mapping with minimum variance distortionless response (MVDR) beamforming and post-filtering to further improve separation, and combine it with frame-level speaker counting for block-online CSS. Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry. State-of-the-art separation performance is obtained on the simulated two-talker SMS-WSJ corpus and the real-recorded LibriCSS dataset.

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