Sound recognition in mixtures

Authors:
Juhan Nam;Gautham J. Mysore;Paris Smaragdis
Affiliations:
Center for Computer Research in Music and Acoustics, Stanford University;Advanced Technology Labs, Adobe Systems Inc.;Advanced Technology Labs, Adobe Systems Inc.
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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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.