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  • 标题:Expectation-maximisation for speech source separation using convolutive transfer function
  • 本地全文:下载
  • 作者:Xiaofei Li ; Laurent Girin ; Radu Horaud
  • 期刊名称:CAAI Transactions on Intelligence Technology
  • 电子版ISSN:2468-2322
  • 出版年度:2019
  • 卷号:4
  • 期号:1
  • 页码:47-53
  • DOI:10.1049/trit.2018.1061
  • 出版社:IET Digital Library
  • 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.
  • 关键词:CTF coefficients; under-determined speech source separation; mixing filters; multichannel microphone signals; room filters; expectation-maximisation algorithm; multiple sources; convolutive transfer function; STFT coefficients; STFT domain; convolutive mixtures; time-domain signals; (STFT) domain; short-time Fourier; widely used narrowband assumption
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