Normalized observation vector clustering approach for sparse source separation

Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji Makino

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

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).

Original languageEnglish
JournalEuropean Signal Processing Conference
Publication statusPublished - 2006
Externally publishedYes
Event14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy
Duration: 2006 Sep 42006 Sep 8

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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