A GMM post-filter for residual crosstalk suppression in blind source separation
Reju, Vaninirappuputhenpurayil Gopalan
Khong, Andy Wai Hoong
Reddy, Vinod Veera
Date of Issue2014
School of Electrical and Electronic Engineering
Existing algorithms employ the Wiener filter to suppress residual crosstalk in the outputs of blind source separation algorithms. We show that, in the context of BSS, the Wiener filter is optimal in the maximum likelihood (ML) sense only for normally-distributed signals. We then propose to model the distribution of speech signals using the Gaussian mixture model (GMM) and then derive a post-filter in the ML sense using the expectation-maximization algorithm. We show that the GMM introduces a probabilistic sample weight that is able to emphasize speech segments that are free of crosstalk components in the BSS output and this results in a better estimate of the post-filter. Simulation results show that the proposed post-filter achieves better crosstalk suppression than the Wiener filter for BSS.
DRNTU::Engineering::Electrical and electronic engineering
IEEE signal processing letters
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2014.2317761].