2006
NeurIPS
NeurIPS 2006
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments
Abstract
We present a method for localizing and separating sound sources in stereo recordings that is robust to reverberation and does not make any assumptions about the source statistics. The method consists of a probabilistic model of binaural multisource recordings and an expectation maximization algorithm for finding the maximum likelihood parameters of that model. These parameters include distributions over delays and assignments of time-frequency regions to sources. We evaluate this method against two comparable algorithms on simulations of simultaneous speech from two or three sources. Our method outperforms the others in anechoic conditions and performs as well as the better of the two in the presence of reverberation.
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Conference Pioneer
- NeurIPS 2006
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Topic Pioneer
- Speech Enhancement
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Interdisciplinary Bridge
- Artificial Intelligence and Speech & Audio
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Trend Setter
- Speech Processing
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Keyword Pioneer
- sound source localization
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Hot Topic Early Bird
- probabilistic model
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Speech & Audio > Recognition > Speech Recognition
Speech & Audio > Synthesis > Speech Enhancement
Speech & Audio > Analysis > Speaker Verification
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Speech & Audio > Analysis > Speech Analysis
Speech & Audio > Processing > Speech Processing