DMANET: Deep Learning-Based Differential Microphone Arrays for Multi-Channel Speech Separation

Xiaokang Yang; Jianguo Wei

ICASSP 2022accepted0 citations

DMANET: Deep Learning-Based Differential Microphone Arrays for Multi-Channel Speech Separation

Abstract

In this paper, we develop a novel differential microphone arrays network (DMANet) for solving the multi-channel speech separation problem. In DMANet we explore a neural network combined to differential microphone arrays (DMAs) beamforming technique. Specifically, a sequence of differential operation is introduced alternately into network. Based on the filter-and-sum network (FaSNet), we show how DMANet significantly improves the separation performance. Numerical experiments demonstrate that the proposed network has a clearly advantageous improvement on SI-SNR with a smaller model.

BibTeX

@inproceedings{icassp2022_dmanetdeeplearni,
  title = {DMANET: Deep Learning-Based Differential Microphone Arrays for Multi-Channel Speech Separation},
  author = {Xiaokang Yang and Jianguo Wei},
  booktitle = {ICASSP 2022},
  year = {2022}
}