Fast Monaural Separation of Speech
|Niels Henrik Pontoppidan, Mads Dyrholm|
|Abstract||We have investigated the possibility of separating signals from a|
single mixture of sources. This problem is termed the Monaural
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.
|Keywords||Monaural, Independent Component Analysis, Blind Source Separation, Factorial Hidden Markov Model, Non-stationarity|
|Type||Conference paper [With referee]|
|Conference||AES 23rd International Conference, Signal Processing in Audio Recording and Reproduction.|
|Year||2003 Month May|
|Publisher||Audio Engineering Society, Inc.|
|Address||60 East 42nd Street, Room 2520, New York, New York, 10165 - 2520 USA|
|BibTeX data|| [bibtex]|
|IMM Group(s)||Intelligent Signal Processing|