Underdetermined blind separation for speech in real environments with sparseness and ICA

Shoko Araki*, Shoji Makino, Audrey Blin, Ryo Mukai, Hiroshi Sawada

*この研究の対応する著者

研究成果査読

40 被引用数 (Scopus)

抄録

In this paper, we propose a method for separating speech signals when there are more signals than sensors. Several methods have already been proposed for solving the underdetermined problem, and some of these utilize the sparseness of speech signals. These methods employ binary masks to extract the signals, and therefore, their extracted signals contain loud musical noise. To overcome this problem, we propose combining a sparseness approach and independent component analysis (ICA). First, using sparseness, we estimate the time points when only one source is active. Then, we remove this single source from the observations and apply ICA to the remaining mixtures. Experimental results show that our proposed sparseness and ICA (SPICA) method can separate signals with little distortion even in reverberant conditions of TR=130 and 200 ms.

本文言語English
ページ(範囲)III881-III884
ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
3
出版ステータスPublished - 2004
外部発表はい
イベントProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
継続期間: 2004 5 172004 5 21

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

フィンガープリント

「Underdetermined blind separation for speech in real environments with sparseness and ICA」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。

引用スタイル