Normalized observation vector clustering approach for sparse source separation

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


研究成果: Conference article査読

6 被引用数 (Scopus)


This paper presents a new method for the blind separation of sparse sources whose number N can exceed the number of sensors M. Recently, sparseness based blind separation has been actively studied. However, most methods utilize a linear sensor array (or only two sensors), and therefore have certain limitations; e.g., they cannot be applied to symmetrically positioned sources. To allow the use of more than two 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. We report promising results for the speech separation of 3-dimensionally distributed five sources with a non-linear/non-uniform array of four sensors in a room (RT 60= 120 ms).

ジャーナルEuropean Signal Processing Conference
出版ステータスPublished - 2006
イベント14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy
継続期間: 2006 9月 42006 9月 8

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学


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