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Title:
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Nonlinear prediction based on score function
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Author:
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Solé-Casals, Jordi; Monte-Moreno, Enric
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Other authors:
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Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals; European Signal Processing Conference (11è : 2002: Toulouse); EUSIPCO 2002 |
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Notes:
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The linear prediction coding of speech is based in the
assumption that the generation model is autoregresive. In
this paper we propose a structure to cope with the
nonlinear effects presents in the generation of the speech
signal. This structure will consist of two stages, the first
one will be a classical linear prediction filter, and the
second one will model the residual signal by means of
two nonlinearities between a linear filter. The coefficients
of this filter are computed by means of a gradient search
on the score function. This is done in order to deal with
the fact that the probability distribution of the residual
signal still is not gaussian. This fact is taken into account
when the coefficients are computed by a ML estimate.
The algorithm based on the minimization of a high-order
statistics criterion, uses on-line estimation of the residue
statistics and is based on blind deconvolution of Wiener
systems [1]. Improvements in the experimental results
with speech signals emphasize on the interest of this
approach. |
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Subject(s):
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-Processament de la parla |
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Rights:
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Aquest document està subjecte a aquesta llicència Creative Commons
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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Document type:
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Conference Object |
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Share:
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