Nonnegative HMM for Babble Noise Derived from Speech HMM: Application to Speech Enhancement
2013 (English)In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, Vol. 21, no 5, 998-1011 p.Article in journal (Refereed) Published
Deriving a good model for multitalker babble noise can facilitate different speech processing algorithms,e.g. noise reduction, to reduce the so-called cocktail party difficulty. In the available systems, thefact that the babble waveform is generated as a sum of N different speech waveforms is not exploitedexplicitly. In this paper, first we develop a gamma hidden Markov model for power spectra of the speechsignal, and then formulate it as a sparse nonnegative matrix factorization (NMF). Second, the sparse NMFis extended by relaxing the sparsity constraint, and a novel model for babble noise (gamma nonnegativeHMM) is proposed in which the babble basis matrix is the same as the speech basis matrix, and only theactivation factors (weights) of the basis vectors are different for the two signals over time. Finally, a noisereduction algorithm is proposed using the derived speech and babble models. All of the stationary modelparameters are estimated using the expectation-maximization (EM) algorithm, whereas the time-varyingparameters, i.e. the gain parameters of speech and babble signals, are estimated using a recursive EMalgorithm. The objective and subjective listening evaluations show that the proposed babble model andthe final noise reduction algorithm significantly outperform the conventional methods.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013. Vol. 21, no 5, 998-1011 p.
Babble noise, hidden Markov model, nonnegative matrix factorization, speech enhancement
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-116767DOI: 10.1109/TASL.2013.2243435ISI: 000315287500003ScopusID: 2-s2.0-84873897366OAI: oai:DiVA.org:kth-116767DiVA: diva2:600801
QC 201302192013-02-192013-01-262013-09-16Bibliographically approved