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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Abramson, A. Cohen, I. |
| Copyright Year | 2006 |
| Abstract | In this paper, we propose a new algorithm for single-sensor audio source separation of speech and music signals, which is based on generalized autoregressive conditional heteroscedasticity (GARCH) modeling of the speech signals and Gaussian mixture modeling (GMM) of the music signals. The separation of the speech from the music signal is obtained by a simultaneous classification and estimation approach, which enables one to control the tradeoff between residual interference and signal distortion. Experimental results on mixtures of speech and piano music signals have yielded an improved source separation performance compared to using Gaussian mixture models for both signals. The tradeoff between signal distortion and residual interference is controlled by adjusting some cost parameters, which are shown to determine the missed and false detection rates in the proposed classification and estimation approach. |
| Sponsorship | IEEE Signal Processing Society |
| Starting Page | 1528 |
| Ending Page | 1540 |
| Page Count | 13 |
| File Size | 688856 |
| File Format | |
| ISSN | 15587916 |
| Volume Number | 16 |
| Issue Number | 8 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Source separation Multiple signal classification Hidden Markov models Speech enhancement Interference Distortion Microphones Costs Instruments Background noise Detection and estimation generalized autoregressive conditional heteroscedasticity (GARCH) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Acoustics and Ultrasonics Electrical and Electronic Engineering |
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