Multi-Channel l_{1} Regularized Convex Speech Enhancement Model and Fast Computation by the Split Bregman Method

Meng Yu Wenye Ma Jack Xin Stanley Osher

Geometric Modeling and Processing mathscidoc:1912.43875

IEEE Transactions on Audio, Speech, and Language Processing, 20, (2), 661-675, 2011.8
A convex speech enhancement (CSE) method is presented based on convex optimization and pause detection of the speech sources. Channel spatial difference is identified for enhancing each speech source individually while suppressing other interfering sources. Sparse unmixing filters indicating channel spatial differences are sought by <i>l</i> <sub>1</sub> norm regularization and the split Bregman method. A subdivided split Bregman method is developed for efficiently solving the problem in severely reverberant environments. The speech pause detection is based on a binary mask source separation method. The CSE method is evaluated objectively and subjectively, and found to outperform a list of existing blind speech separation approaches on both synthetic and room recorded speech mixtures in terms of the overall computational speed and separation quality.
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@inproceedings{meng2011multi-channel,
  title={Multi-Channel l_{1} Regularized Convex Speech Enhancement Model and Fast Computation by the Split Bregman Method},
  author={Meng Yu, Wenye Ma, Jack Xin, and Stanley Osher},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210352690093439},
  booktitle={IEEE Transactions on Audio, Speech, and Language Processing},
  volume={20},
  number={2},
  pages={661-675},
  year={2011},
}
Meng Yu, Wenye Ma, Jack Xin, and Stanley Osher. Multi-Channel l_{1} Regularized Convex Speech Enhancement Model and Fast Computation by the Split Bregman Method. 2011. Vol. 20. In IEEE Transactions on Audio, Speech, and Language Processing. pp.661-675. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210352690093439.
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