Abstract
In this paper, we propose a new blind speech extraction method combining ICA-based dynamic noise estimation and a generalized minimum mean-square-error short-time spectral amplitude estimator of the target speech. To deal with various types of speech signals with different probability density functions (p.d.f.), we also introduce a spectral-subtraction-based speech p.d.f. estimation and provide a theoretical justification of the proposed approach. We conduct an experiment in an actual railway-station environment, and show the improved noise reduction of the proposed method by objective and subjective evaluations.
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Saruwatari, H., Okamoto, R., Takahashi, Y., Shikano, K. (2010). Blind Speech Extraction Combining Generalized MMSE STSA Estimator and ICA-Based Noise and Speech Probability Density Function Estimations. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_7
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DOI: https://doi.org/10.1007/978-3-642-15995-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
Online ISBN: 978-3-642-15995-4
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