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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Balan, R. Rosca, J. |
| Copyright Year | 2006 |
| Description | Author affiliation: Siemens Corp. Res., Princeton, NJ (Balan, R.; Rosca, J.) |
| Abstract | In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with a deterministic but unknown spectral amplitude. The Bernoulli variables are modeled in turn by first order Markov processes with transition probabilities learned from a training database. We consider a video conferencing scenario where the mixing parameters are estimated by the video system. We obtain the MAP signal estimators and show they are implemented by a Vitterbi decoding scheme. We validate this approach by simulations using TIMIT database, and compare the separation performance of this algorithm with our previous extended DUET method |
| Sponsorship | The Inst. of Electr. and Electron. Eng., Inc. Circuits and Syst. Soc. (IEEE CASS) |
| File Size | 260565 |
| File Format | |
| ISBN | 142440469X |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2006.1661168 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-05-14 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Source separation Signal processing algorithms Time frequency analysis Frequency domain analysis Databases Videoconference Hidden Markov models Random variables Independent component analysis Speech |
| Content Type | Text |
| Resource Type | Article |
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