| Paper: | MLSP-L2.1 | ||
| Session: | Blind Source Separation | ||
| Time: | Friday, May 21, 13:00 - 13:20 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | NATURAL GRADIENT MULTICHANNEL BLIND DECONVOLUTION AND SOURCE SEPARATION USING CAUSAL FIR FILTERS | ||
| Authors: | Scott Douglas; Southern Methodist University | ||
| Hiroshi Sawada; NTT Corporation | |||
| Shoji Makino; NTT Corporation | |||
| Abstract: | Practical gradient-based adaptive algorithms for multichannel blind deconvolution and convolutive blind source separation typically employ FIR filters for the separation system. Inadequate use of signal truncation within these algorithms can introduce steady-state biases into their converged solutions that lead to degraded separation anddeconvolution performances. In this paper, we derive a natural gradient multichannel blind deconvolution and source separation algorithm that mitigates these effects for estimating causal FIR solutions to these tasks. Numerical experiments verify the robust convergence performance of the new method both in multichannel blind deconvolution tasks for i.i.d. sources and in convolutive BSS tasks for acoustic sources, even for extremely-short separation filters. | ||
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