NMF-based environmental sound source separation using time-variant gain features

Satoshi Innami, Hiroyuki Kasai

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Various environmental sounds exist around us in our daily life. Recently, environmental sound recognition has drawn great attention for understanding our environment. However, because environmental sounds derive from multiple sound sources, it is difficult to recognize them accurately. If we were able to separate sound sources before sound recognition as a pre-process, then recognition would be easier and more accurate. We assume that monaural microphones are widely installed in mobile devices used as recording devices. This paper therefore presents a proposal for monaural sound source separation of environmental sounds. Two-phase clustering using non-negative matrix factorization (NMF) is proposed to separate monaural sound sources. In this proposal, the time-variant gain feature is used as an attribute of an environmental sound for more efficient sound separation.

Original languageEnglish
Pages (from-to)1333-1342
Number of pages10
JournalComputers and Mathematics with Applications
Volume64
Issue number5
DOIs
Publication statusPublished - 2012 Sep
Externally publishedYes

Keywords

  • Acoustic signal analysis
  • Audio source separation
  • Blind source separation
  • Clustering
  • Environmental sound
  • Non-negative matrix factorization

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics

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