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Abstract: In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly proved when regression analysis is performed on the sparse, non-negative features, both compared to linear regression on spectral features and compared to separation based directly on the non-negative sparse features.
Demonstration: Here are a few audio samples demonstrating the MAP-SNMF-5 algorithm described in the paper.
| Mixture | Speaker 1 | Speaker 2 | |
|---|---|---|---|
| Male-Female |
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| Male-Male |
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| Female-Female |
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- See also:
Reduction of Non-stationary Noise using a Non-negative Latent Variable Decomposition- Feature Space Reconstruction for Single-Channel Speech Separation
- Files:
imm5272.pdf- waspaa2007_poster.pdf
- Cite:
- Mikkel N. Schmidt and Rasmus K. Olsson, Linear Regression on Sparse Features for Single-Channel Speech Separation, Applications of Signal Processing to Audio and Acoustics, IEEE Workshop on (WASPAA), 2007
- BibTeX:
- @article{schmidt07waspaa,
title = "Linear Regression on Sparse Features for Single-Channel Speech Separation",
author = "Mikkel N. Schmidt and Rasmus K. Olsson",
booktitle = "Applications of Signal Processing to Audio and Acoustics, IEEE Workshop on (WASPAA)",
year = "2007"
}