Speech Enhancement by Online Non-negative Spectrogram Decomposition in Non-stationary Noise Environments


Published September 9, 2012

Zhiyao Duan, Gautham Mysore, Paris Smaragdis

Classical single-channel speech enhancement algorithms have two convenient properties: they require pre-learning the noise model but not the speech model, and they work online. However, they often have difficulties in dealing with non-stationary noise sources. Source separation algorithms based on nonnegative spectrogram decompositions are capable of dealing with non-stationary noise, but do not possess the aforementioned properties. In this paper we present a novel algorithm that combines the advantages of both classical algorithms and non-negative spectrogram decomposition algorithms. Experiments show that it significantly outperforms four categories of classical algorithms in non-stationary noise environments.

Research Areas:  AI & Machine Learning Audio