Sound recognition in mixtures

Juhan Nam, Gautham J. Mysore, Paris Smaragdis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we describe a method for recognizing sound sources in a mixture. While many audio-based content analysis methods focus on detecting or classifying target sounds in a discriminative manner, we approach this as a regression problem, in which we estimate the relative proportions of sound sources in the given mixture. Using source separation ideas based on probabilistic latent component analysis, we directly estimate these proportions from the mixture without actually separating the sources. We also introduce a method for learning a transition matrix to temporally constrain the problem. We demonstrate the proposed method on a mixture of five classes of sounds and show that it is quite effective in correctly estimating the relative proportions of the sounds in the mixture.

Original languageEnglish (US)
Title of host publicationLatent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
Pages405-413
Number of pages9
DOIs
StatePublished - Feb 27 2012
Event10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel
Duration: Mar 12 2012Mar 15 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7191 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
CountryIsrael
CityTel Aviv
Period3/12/123/15/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Nam, J., Mysore, G. J., & Smaragdis, P. (2012). Sound recognition in mixtures. In Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings (pp. 405-413). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7191 LNCS). https://doi.org/10.1007/978-3-642-28551-6_50