Frequency domain blind source separation of a reduced amount of data using frequency normalization

Enrique Robledo-Arnuncio*, Hiroshi Sawada, Shoji Makino

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

The problem of blind source separation (BSS) from convolutive mixtures is often addressed using independent component analysis in the frequency domain. The separation performance with this approach degrades significantly when only a short amount of data is available, since the estimation of the separation system becomes inaccurate. In this paper we present a novel approach to the frequency domain BSS using frequency normalization. Under the conditions of almost sparse sources and of dominant direct path in the mixing systems, we show that the new approach provides better performance than the conventional one when the amount of available data is small.

本文言語English
ホスト出版物のタイトル2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
ページV837-V840
出版ステータスPublished - 2006
外部発表はい
イベント2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
継続期間: 2006 5 142006 5 19

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
5
ISSN(印刷版)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
国/地域France
CityToulouse
Period06/5/1406/5/19

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

「Frequency domain blind source separation of a reduced amount of data using frequency normalization」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