Underdetermined blind separation of convolutive mixtures of speech using time-frequency mask and mixing matrix estimation

Audrey Blin*, Shoko Araki, Shoji Makino


研究成果: Review article査読

19 被引用数 (Scopus)


This paper focuses on the underdetermined blind source separation (BSS) of three speech signals mixed in a real environment from measurements provided by two sensors. To date, solutions to the underdetermined BSS problem have mainly been based on the assumption that the speech signals are sufficiently sparse. They involve designing binary masks that extract signals at time-frequency points where only one signal was assumed to exist. The major issue encountered in previous work relates to the occurrence of distortion, which affects a separated signal with loud musical noise. To overcome this problem, we propose combining sparseness with the use of an estimated mixing matrix. First, we use a geometrical approach to detect when only one source is active and to perform a preliminary separation with a time-frequency mask. This information is then used to estimate the mixing matrix, which allows us to improve our separation. Experimental results show that this combination of time-frequency mask and mixing matrix estimation provides separated signals of better quality (less distortion, less musical noise) than those extracted without using the estimated mixing matrix in reverberant conditions where the reverberant time (TR) was 130 ms and 200 ms. Furthermore, informal listening tests clearly show that musical noise is deeply lowered by the proposed method comparatively to the classical approaches.

ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
出版ステータスPublished - 2005 7月

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学


「Underdetermined blind separation of convolutive mixtures of speech using time-frequency mask and mixing matrix estimation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。