| Paper: | MLSP-P5.3 | ||
| Session: | Blind Source Separation III | ||
| Time: | Friday, May 19, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | Frequency Domain Blind Source Separation of a Reduced Amount of Data Using Frequency Normalization | ||
| Authors: | Enrique Robledo-Arnuncio, Hiroshi Sawada, Shoji Makino, NTT Corporation, Japan | ||
| Abstract: | The problem of blind source separation (BSS) from convolutive mixtures is often addressed using independent component analysis in the frequency domain. The separation performance with this approach degrades significantly when only a short amount of data is available, since the estimation of the separation system becomes inaccurate. In this paper we present a novel approach to the frequency domain BSS using frequency normalization. Under the conditions of almost sparse sources and of dominant direct path in the mixing systems, we show that the new approach provides better performance than the conventional one when the amount of available data is small. | ||
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