PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Reduction of Non-stationary Noise using a Non-negative Latent Variable Decomposition
Mikkel N. Schmidt and Jan Larsen
In: Machine Learning for Signal Processing, IEEE Workshop on (MLSP), 2008(2008).

Abstract

We present a method for suppression of non-stationary noise in single channel recordings of speech. The method is based on nonnegative sparse coding and relies on a voice activity detector. In regions classified as non-speech, we learn an overcomplete basis for the noise which is then used to estimate the speech and the noise from the mixture. We compare the method to the classical approach where the noise spectrum is estimated as the average of non-speech frames. The proposed method significantly outperforms the classic approach when the noise is highly non-stationary.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Speech
ID Code:6519
Deposited By:Mikkel Schmidt
Deposited On:08 March 2010