Online noise estimation using stochastic-gain HMM for speech enhancement
2008 (English)In: IEEE transactions on speech and audio processing, ISSN 1063-6676, E-ISSN 1558-2353, Vol. 16, no 4, 835-846 p.Article in journal (Refereed) Published
We propose a noise estimation algorithm for single-channel noise suppression in dynamic noisy environments. A stochastic-gain hidden Markov model (SG-HMM) is used to model the statistics of nonstationary noise with time-varying energy. The noise model is adaptive and the model parameters are estimated online from noisy observations using a recursive estimation algorithm. The parameter estimation is derived for the maximum-likelihood criterion and the algorithm is based on the recursive expectation maximization (EM) framework. The proposed method facilitates continuous adaptation to changes of both noise spectral shapes and noise energy levels, e.g., due to movement of the noise source. Using the estimated noise model, we also develop an estimator of the noise power spectral density (PSD) based on recursive averaging of estimated noise sample spectra. We demonstrate that the proposed scheme achieves more accurate estimates of the noise model and noise PSD, and as part of a speech enhancement system facilitates a lower level of residual noise.
Place, publisher, year, edition, pages
2008. Vol. 16, no 4, 835-846 p.
gain modeling; noise estimation; noise model adaptation; noise suppression; stochastic-gain hidden Markov model (SG-HMM)
IdentifiersURN: urn:nbn:se:kth:diva-7236DOI: 10.1109/TASL.2008.916055ISI: 000258033600014ScopusID: 2-s2.0-64849085665OAI: oai:DiVA.org:kth-7236DiVA: diva2:12188
QC 20100825. Uppdaterad från Submitted till Published 20100825.2007-05-312007-05-312010-08-25Bibliographically approved