| Paper: | SAM-P5.6 | ||
| Session: | Source Detection, Estimation and Separation | ||
| Time: | Friday, May 19, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Source localization, separation, classification, and tracking | ||
| Title: | SOURCE SEPARATION USING SPARSE DISCRETE PRIOR MODELS | ||
| Authors: | Radu Balan, Justinian Rosca, Siemens Corporate Research, United States | ||
| 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. | ||
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