Simultaneous Noise Classification And Reduction Using A Priori Learned Models
Nasser Mohammadiha, Paris Smaragdis, Arne Leijon

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.