In this paper, we develop a multi-channel noise reduction algorithm based on blind source separation (BSS). In contrast to general BSS algorithms that attempt to recover all the signals, we explicitly estimate only the speech signal. By tracking the minimum of the spectral density of the microphone signals, noise-only segments are identified. The coefficients of the unmixing matrix that are necessary to separate the speech are identified from these segments through the optimization of an appropriate energy criterion. Since the proposed method explicitly estimates the speech signal from the noisy mixture, it does not suffer from the permutation problem that is typical to conventional BSS techniques. The method is applicable to both instantaneous and convolutive mixtures and achieves the separation in a single step, without the need for iterations. Experimental results show superior performance compared to a general BSS algorithm.
Cite as: Srinivasan, S., Nilsson, M., Kleijn, W.B. (2005) Denoising through source separation and minimum tracking. Proc. Interspeech 2005, 2349-2352, doi: 10.21437/Interspeech.2005-748
@inproceedings{srinivasan05b_interspeech,
author={Sriram Srinivasan and Mattias Nilsson and W. Bastiaan Kleijn},
title={{Denoising through source separation and minimum tracking}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={2349--2352},
doi={10.21437/Interspeech.2005-748}
}