Abstract
In this paper, we present an on-line semi-supervised algorithm for real-time separation of speech and background noise. The proposed system is based on Nonnegative Matrix Factorization (NMF), where fixed speech bases are learned from training data whereas the noise components are estimated in real-time on the recent past.
Experiments with spontaneous conversational speech and real-life non-stationary noise show that this system performs as well as a supervised NMF algorithm exploiting noise components learned from the same noise environment as the test sample. Furthermore, it outperforms a supervised system trained on different noise conditions.
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Joder, C., Weninger, F., Eyben, F., Virette, D., Schuller, B. (2012). Real-Time Speech Separation by Semi-supervised Nonnegative Matrix Factorization. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_40
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DOI: https://doi.org/10.1007/978-3-642-28551-6_40
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