Source extraction in audio via background learning

Yang Wang; Zhengfang Zhou

doi:10.3934/ipi.2013.7.283

Article Contents

2013, Volume 7, Issue 1: 283-290. Doi: 10.3934/ipi.2013.7.283

This issue Previous Article Absorption and phase retrieval with Tikhonov and joint sparsity regularizations Next Article A decomposition method for an interior inverse scattering problem

Source extraction in audio via background learning

Yang Wang^1, and
Zhengfang Zhou^2,

1.
Department of Mathematics, Michigan State University, East Lanisng, MI 48824
2.
Department of Mathematics, Michigan State University, East Lansing, MI 48824

Received: July 2010

Revised: October 2010

Published: February 2013

Abstract / Introduction Related Papers Cited by

Abstract

Source extraction in audio is an important problem in the study of blind source separation (BSS) with many practical applications. It is a challenging problem when the foreground sources to be extracted are weak compared to the background sources. Traditional techniques often do not work in this setting. In this paper we propose a novel technique for extracting foreground sources. This is achieved by an interval of silence for the foreground sources. Using this silence interval one can learn the background information, allowing the removal or suppression of background sources. Very effective optimization schemes are proposed for the case of two sources and two mixtures.

Keywords:

Mathematics Subject Classification: Primary: 94A12, 65T99.

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