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Learning speech features in the presence of noise: Sparse convolutive robust non-negative matrix factorization

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2 Author(s)
Ruairi de Frein ; Complex and Adaptive Systems Laboratory, University College Dublin, Ireland ; Scott T. Rickard

We introduce a non-negative matrix factorization technique which learns speech features with temporal extent in the presence of non-stationary noise. Our proposed technique, namely Sparse convolutive robust non-negative matrix factorization, is robust in the presence of noise due to our explicit treatment of noise as an interfering source in the factorization. We derive multiplicative update rules using the alpha divergence objective. We show that our proposed method yields superior performance to sparse convolutive non-negative matrix factorization in a feature learning task on noisy data and comparable results to dedicated speech enhancement techniques.

Published in:

2009 16th International Conference on Digital Signal Processing

Date of Conference:

5-7 July 2009