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Deep Complex Convolutional Recurrent Network for Multi-Channel Speech Enhancement and Dereverberation

Abstract

This paper proposes a neural network based system for multi-channel speech enhancement and dereverberation. Speech recorded indoors by a far field microphone, is invariably degraded by noise and reflections. Recent single channel enhancement systems have improved denoising performance, but do not reduce reverberation, which also reduces speech quality and intelligibility. To address this, we propose a deep complex convolution recurrent network (DCCRN) based multi-channel system, with integrated minimum power distortionless response (MPDR) beamformer and weighted prediction error (WPE) preprocessing.

PESQ and STOI performance is evaluated on a test set of room impulse responses and noise samples recorded by the same setup. The proposed system shows a statistically significant improvement over competitive systems.

Category

Academic chapter/article/Conference paper

Language

English

Author(s)

Affiliation

  • Norwegian University of Science and Technology
  • SINTEF Digital / Connectivity Technologies and Platforms

Year

2021

Publisher

IEEE

Book

2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)

Issue

31

ISBN

978-1-7281-6338-3

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