Expectation-maximisation for speech source separation using convolutive transfer function

• 作者: Laurent Girin Radu Horaud Xiaofei Li
• 来源期刊: 智能技术学报
• 学科: 社会科学
• 关键词: STFT CTF
• 年，卷（期）: 2019,（1）
• 页码范围: 47-53
• 页数: 7页
• 分类号: G

摘要

This study addresses the problem of under-determined speech source separation from multichannel microphone signals,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,the authors 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 maximising the likelihood of the microphone signals,which is resolved by an expectation-maximisation algorithm.Experiments show that the proposed method provides very satisfactory performance under highly reverberant environments.