Reduction of non-stationary noise using a non-negative latent variable decomposition |
Mikkel N. Schmidt, Jan Larsen
|
| Abstract | We present amethod for suppression of non-stationary noise
in single channel recordings of speech. Themethod is based
on a non-negative latent variable decomposition model for
the speech and noise signals, learned directly from a noisy
mixture. In non-speech regions an overcomplete basis is
learned for the noise that is then used to jointly estimate
the speech and the noise from the mixture. We compare
the method to the classical spectral subtraction approach,
where the noise spectrum is estimated as the average over
non-speech frames. The proposed method significantly outperforms
the classic approach, especially when the noise is
highly non-stationary and at low signal-to-noise ratios. |
| Keywords | single channel noise reduction, latent variable, bayes, non-negative factorization |
| Type | Misc [Presentation] |
| Journal/Book/Conference | IEEE Workshop on Machine Learning for Signal Processing 2008 |
| Year | 2008 Month October |
| Publisher | Department of Informatics and Mathematical Modelling |
| Address | Richard Petersens Plads, Building 321 |
| Note | Presented at MLSP2008 19.10.2008 |
| Electronic version(s) | [pdf] |
| Publication link | http://mlsp2008.conwiz.dk |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |