| Paper: | MLSP-L2.2 | ||
| Session: | Blind Source Separation | ||
| Time: | Friday, May 21, 13:20 - 13:40 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | BLIND SOURCE SEPARATION OF NONSTATIONARY CONVOLUTIVELY MIXED SIGNALS IN THE SUBBAND DOMAIN | ||
| Authors: | Iain Russell; University of Wollongong | ||
| Jiangtao Xi; University of Wollongong | |||
| Alfred Mertins; University of Oldenburg | |||
| Joe Chicharo; University of Wollongong | |||
| Abstract: | This paper proposes a new technique for blind source separation (BSS) in the subband domain using an extended lapped transform (ELT) decomposition for non-stationary, convolutively mixed signals. As identified in [1] the motivation for subband-based BSS is the drawback of frequency domain BSS when dealing with separating mixed speech signals over a few seconds resulting with few samples in individual frequency bins leading to poor separation performance. In the proposed approach mixed signals are decomposed into subband components by an ELT and within each subband a time domain Newton BSS algorithm is employed based on the non-stationary property of the input signals and the joint diagonalization of output correlation matrices with time varying second order statistics (SOS). This subband version is compared to a fullband version using the same BSS algorithm. | ||
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