Re-estimation of linear predictive parameters in sparse linear prediction
Daniele Giacobello, Manohar N. Murthi, Mads Græsbøll Christensen, Søren Holdt Jensen, Marc Moonen · 2009
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.