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A dynamic algorithm for blind separation of convolutive sound mixtures

Jie Liu Jack Xin Yingyong Qi

Geometric Modeling and Processing mathscidoc:1912.43866

Neurocomputing, 72, 521-532, 2008.12
We study an efficient dynamic blind source separation algorithm of convolutive sound mixtures based on updating statistical information in the frequency domain, and minimizing the support of time domain demixing filters by a weighted least square method. The permutation and scaling indeterminacies of separation, and concatenations of signals in adjacent time frames are resolved with optimization of l 1 l norm on cross-correlation coefficients at multiple time lags. The algorithm is a direct method without iterations, and is adaptive to the environment. Computations on recorded benchmark mixtures of speech and music signals show excellent performance. The method in general separates a foreground source from a background of sounds as often encountered in realistic situations.
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@inproceedings{jie2008a,
  title={A dynamic algorithm for blind separation of convolutive sound mixtures},
  author={Jie Liu, Jack Xin, and Yingyong Qi},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210311800127430},
  booktitle={Neurocomputing},
  volume={72},
  pages={521-532},
  year={2008},
}
Jie Liu, Jack Xin, and Yingyong Qi. A dynamic algorithm for blind separation of convolutive sound mixtures. 2008. Vol. 72. In Neurocomputing. pp.521-532. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210311800127430.
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