Simultaneous Noise Classification and Reduction Using a Priori Learned Models
2013 (English)In: 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), IEEE Signal Processing Society, 2013, 6661951- p.Conference paper (Refereed)
Classifying the acoustic environment is an essential part of a practical supervised source separation algorithm where a model is trained for each source offline. In this paper, we present a classification scheme that is combined with a probabilistic nonnegative matrix factorization (NMF) based speech denoising algorithm. We model the acoustic environment with a hidden Markov model (HMM) whose emission distributions are assumed to be of NMF type. We derive a minimum mean square error (MMSE) estimator of clean speech signal in which the state-dependent speech estimators are weighted according to the state posterior probabilities (or probabilities of different noise environments) and are summed. Our experiments show that the proposed method outperforms state-of-the-art substantially and that its performance is very close to an oracle case where the noise type is known in advance.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013. 6661951- p.
, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, ISSN 2161-0363
acoustic environment classification, Nonnegative matrix factorization, supervised speech enhancement
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-124648DOI: 10.1109/MLSP.2013.6661951ISI: 000345844100057ScopusID: 2-s2.0-84893272779ISBN: 978-147991180-6OAI: oai:DiVA.org:kth-124648DiVA: diva2:637965
2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013; Southampton; United Kingdom; 22 September 2013 through 25 September 2013
QC 201307242013-07-242013-07-242015-01-07Bibliographically approved