Underdetermined sparse source separation of convolutive mixtures with observation vector clustering

Shoko Arakiti, Hiroshi Sawada, Ryo Mukai, Shoji Makino

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

We propose a new method for solving the underdetermined sparse signal separation problem. Some sparseness based methods have already been proposed. However, most of these methods utilized a linear sensor array (or only two sensors), and therefore they have certain limitations; e.g., they cannot separate symmetrically positioned sources. To allow the use of more than three sensors that can be arranged in a non-linear/non-uniform way, we propose a new method that includes the normalization and clustering of the observation vectors. Our proposed method can handle both underdetermined case and (over-)determined cases. We show practical results for speech separation with nonlinear/non-uniform sensor arrangements. We obtained promising experimental results for the cases of 3 × 4, 4 × 5 (#sensors × #sources) in a room (RT60 = 120 ms).

本文言語English
ホスト出版物のタイトルISCAS 2006
ホスト出版物のサブタイトル2006 IEEE International Symposium on Circuits and Systems, Proceedings
ページ3594-3597
ページ数4
出版ステータスPublished - 2006
外部発表はい
イベントISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece
継続期間: 2006 5 212006 5 24

出版物シリーズ

名前Proceedings - IEEE International Symposium on Circuits and Systems
ISSN(印刷版)0271-4310

Conference

ConferenceISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
CountryGreece
CityKos
Period06/5/2106/5/24

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

フィンガープリント 「Underdetermined sparse source separation of convolutive mixtures with observation vector clustering」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