Real-Time Speech Separation by Semi-supervised Nonnegative Matrix Factorization

@inproceedings{Joder2012RealTimeSS,
  title={Real-Time Speech Separation by Semi-supervised Nonnegative Matrix Factorization},
  author={Cyril Joder and Felix Weninger and Florian Eyben and David Virette and Bj{\"o}rn W. Schuller},
  booktitle={LVA/ICA},
  year={2012}
}
In this paper, we present an on-line semi-supervised algorithm for real-time separation of speech and background noise. The proposed system is based on Nonnegative Matrix Factorization (NMF), where fixed speech bases are learned from training data whereas the noise components are estimated in real-time on the recent past. Experiments with spontaneous conversational speech and real-life nonstationary noise show that this system performs as well as a supervised NMF algorithm exploiting noise… CONTINUE READING
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