| Paper: | MLSP-L2.4 | ||
| Session: | Blind Source Separation | ||
| Time: | Friday, May 21, 14:00 - 14:20 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | A METHOD FOR DIRECTIONALLY-DISJOINT SOURCE SEPARATION IN CONVOLUTIVE ENVIRONMENT | ||
| Authors: | Shlomo Dubnov; University of California, San Diego | ||
| Joseph Tabrikian; Ben-Gurion University | |||
| Miki Arnan-Targan; Ben-Gurion University | |||
| Abstract: | In this paper we propose a new method for source separation that is based on directionally-disjoint estimation of the transfer functions between microphones and sources at different frequencies and at multiple times. Smoothing and association of transfer function parameters across different frequencies is achieved by simultaneous Kalman filtering of the noisy amplitude and phase estimates. This approach allows estimating transfer functions even in the case where the difference between the sources is in delay only and it can operate both for wideband and narrowband sources. Simulation results show superior performance to other existing methods. | ||
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