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2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers

DOI: 10.1109/acssc.2009.5470202

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Re-estimation of Linear Predictive Parameters in Sparse Linear Prediction

Journal article published in 2009 by Daniele Giacobello, Manohar N. Murthi, Mads Graesboll Christensen ORCID, Søren Holdt Jensen, Marc Moonen
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This paper is available in a repository.

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Abstract

In this work, we propose a novel scheme to re-estimate the linear predictive parameters in sparse speech coding. The idea is to estimate the optimal truncated impulse response that creates the given sparse coded residual without distortion. An all-pole approximation of this impulse response is then found using a least square approximation. The all-pole approximation is a stable linear predictor that allows a more efficient reconstruction of the segment of speech. The effectiveness of the algorithm is proved in the experimental analysis.