Sparse reverberant audio source separation via reweighted analysis

Simon Arberet, Pierre Vandergheynst, Rafael E Carrillo, Jean-Philippe Thiran, Yves Wiaux

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

We propose a novel algorithm for source signals estimation from an underdetermined convolutive mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant mixture, assuming a synthesis sparse prior in the time-frequency domain and a narrowband approximation of the convolutive mixing process. In this paper, we address the source estimation of convolutive mixtures with a new algorithm based on i) an analysis sparse prior, ii) a reweighting scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a constrained form. We show, through theoretical discussions and simulations, that this algorithm is particularly well suited for source separation of realistic reverberation mixtures. Particularly, the proposed algorithm outperforms state-of-the-art methods on reverberant mixtures of audio sources by more than 2 dB of signal-to-distortion ratio on the BSS Oracle dataset.

Original languageEnglish
Article number6473837
Pages (from-to)1391-1402
Number of pages12
JournalIEEE Transactions on Audio, Speech, and Language Processing
Volume21
Issue number7
DOIs
Publication statusPublished - Jul 2013

Keywords

  • convex optimization
  • Convolutive mixture
  • source separation
  • sparsity

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics

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