Speech Enhancement using Multi-Microphone Array based Source Separation and Deep Learning

Abstract

Speech Enhancement using tensor decomposition-based source separation and convolutional, bidirectional recurrent neural network (CNN-biRNN) architecture is investigated in this paper. An acoustic receiver comprising uniform linear array (ULA) of microphone sensors is considered, where the ULA performs CANDECOMP/PARAFAC (CP) tensor decomposition to separate the individual speech source signals from the received mixture of multi-channel signals, followed by single channel de-reverberation by a variant of the CNN-biRNN referred to as DenseNet-biLSTM to enhance the target speech signal-of-interest (SOI). While the source separation module based on CP-tensor decomposition is responsible for extracting the target SOI, the subsequent deep learning framework based on DenseNet-biLSTM enhances the extracted SOI by performing de-noising and de-reverberation. It is demonstrated by computer simulations that the proposed approach leads to good performance under multiple interfering speakers and reverberation.