Abstract
This paper presents a new statistical model for speech signals, which consists of a time-invariant dictionary incorporating a set of the power spectral densities of excitation signals and a set of all-pole filters where the gain of each pair of excitation and filter elements is allowed to vary over time. We use this model to develop a combined blind separation and dereverberation method for speech. Reasonably good separations were obtained under a highly reverberant condition.
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Kameoka, H., Yoshioka, T., Hamamura, M., Le Roux, J., Kashino, K. (2010). Statistical Model of Speech Signals Based on Composite Autoregressive System with Application to Blind Source Separation. 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_31
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DOI: https://doi.org/10.1007/978-3-642-15995-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
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