Re-estimation of linear predictive parameters in sparse linear prediction

Authors:
Daniele Giacobello;Manohar N. Murthi;Mads Græsbøll Christensen;Søren Holdt Jensen;Marc Moonen
Affiliations:
Dept. of Electronic Systems, Aalborg Universitet, Aalborg, Denmark;Dept. of Electrical and Computer Engineering, University of Miami;Dept. of Media Technology, Aalborg Universitet, Aalborg, Denmark;Dept. of Electronic Systems, Aalborg Universitet, Aalborg, Denmark;Dept. of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

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Abstract

In this work, we propose a novel scheme to reestimate 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.