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NI. H.. Pontoppidan, and M. Dyrholm, "Fast Monaural Separation of Speech," Paper 6, (2003 May.). doi: NI. H.. Pontoppidan, and M. Dyrholm, "Fast Monaural Separation of Speech," Paper 6, (2003 May.). doi:
Abstract: We have investigated the possibility of separating signals from a single mixture of sources. This problem is termed the Monaural Separation Problem. Lars Kai Hansen has argued that this problem is topological tougher than problems with multiple recordings. Roweis has shown that inference from a Factorial Hidden Markov Model, with non-stationary assumptions on the source autocorrelations modelled through the Factorial Hidden Markov Model, leads to separation in the monaural case. By extending Hansens work we find that Roweis' assumptions are necessary for monaural speech separation. Furthermore we develop a Factorial hierarchical vector quantizer yielding a significant decrease in complexity of inference.

@article{pontoppidan2003fast,
author={pontoppidan, niels h. and dyrholm, mads},
journal={journal of the audio engineering society},
title={fast monaural separation of speech},
year={2003},
volume={},
number={},
pages={},
doi={},
month={may},} @article{pontoppidan2003fast,
author={pontoppidan, niels h. and dyrholm, mads},
journal={journal of the audio engineering society},
title={fast monaural separation of speech},
year={2003},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={we have investigated the possibility of separating signals from a single mixture of sources. this problem is termed the monaural separation problem. lars kai hansen has argued that this problem is topological tougher than problems with multiple recordings. roweis has shown that inference from a factorial hidden markov model, with non-stationary assumptions on the source autocorrelations modelled through the factorial hidden markov model, leads to separation in the monaural case. by extending hansens work we find that roweis' assumptions are necessary for monaural speech separation. furthermore we develop a factorial hierarchical vector quantizer yielding a significant decrease in complexity of inference.},}

TY - paper
TI - Fast Monaural Separation of Speech
SP - EP -
AU - Pontoppidan, Niels H.
AU - Dyrholm, Mads
PY - 2003
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2003 TY - paper
TI - Fast Monaural Separation of Speech
SP - EP -
AU - Pontoppidan, Niels H.
AU - Dyrholm, Mads
PY - 2003
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2003
AB - We have investigated the possibility of separating signals from a single mixture of sources. This problem is termed the Monaural Separation Problem. Lars Kai Hansen has argued that this problem is topological tougher than problems with multiple recordings. Roweis has shown that inference from a Factorial Hidden Markov Model, with non-stationary assumptions on the source autocorrelations modelled through the Factorial Hidden Markov Model, leads to separation in the monaural case. By extending Hansens work we find that Roweis' assumptions are necessary for monaural speech separation. Furthermore we develop a Factorial hierarchical vector quantizer yielding a significant decrease in complexity of inference.