TR2007-062

Supervised and Semi-Supervised Separation of Sounds from Single-Channel Mixtures
Date:July 2006
MERL Contact:Bhiksha Raj
Author:Paris Smaragdis, Bhiksha Raj, madhusudana Shashanka
Where Published:International Conference on Independent Component Analysis and Signal Separation

In this paper we describe a methodology for model-based
single channel separation of sounds. We present a sparse latent variable
model that can learn sounds based on their distribution of time/frequency
energy. This model can then be used to extract known types of sounds
from mixtures in two scenarios. One being the case where all sound types
in the mixture are known, and the other being being the case where only
the target or the interference models are known. The model we propose
has close ties to non-negative decompositions and latent variable models
commonly used for semantic analysis.

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