Expectation-Maximization for Speech Source Separation Using Convolutive Transfer Function
Abstract
This paper addresses the problem of under-determinded speech source separation from multichannel microphone singals, i.e. the convolutive mixtures of multiple sources. The time-domain signals are first transformed to the short-time Fourier transform (STFT) domain. To represent the room filters in the STFT domain, instead of the widely-used narrowband assumption, we propose to use a more accurate model, i.e. the convolutive transfer function (CTF). At each frequency band, the CTF coefficients of the mixing filters and the STFT coefficients of the sources are jointly estimated by maximizing the likelihood of the microphone signals, which is resolved by an Expectation-Maximization (EM) algorithm. Experiments show that the proposed method provides very satisfactory performance under highly reverberant environments.
Cite
@article{arxiv.1904.05249,
title = {Expectation-Maximization for Speech Source Separation Using Convolutive Transfer Function},
author = {Xiaofei Li and Laurent Girin and Radu Horaud},
journal= {arXiv preprint arXiv:1904.05249},
year = {2019}
}