Online noise estamation using stochastic-gain HMM for speech enhancement

D.Y. Zhao, W.B. Kleijn, A. Ypma, B. Vries, de

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)
208 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)835-846
Number of pages12
JournalIEEE Transactions on Audio, Speech, and Language Processing
Issue number4
Publication statusPublished - 2008


Dive into the research topics of 'Online noise estamation using stochastic-gain HMM for speech enhancement'. Together they form a unique fingerprint.

Cite this