Joint estimation of short-term and long-term predictors in speech coders

  • Authors:

  • Daniele Giacobello;Mads Graesboll Christensen;Joachim Dahl;Soren Holdt Jensen;Marc Moonen

  • Affiliations:

  • Dept. of Electronic Systems (ES-MISP), Aalborg University, Denmark;Dept. of Electronic Systems (ES-MISP), Aalborg University, Denmark;Dept. of Electronic Systems (ES-MISP), Aalborg University, Denmark;Dept. of Electronic Systems (ES-MISP), Aalborg University, Denmark;Dept. of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

In low bit-rate coders, the near-sample and far-sample redundancies of the speech signal are usually removed by a cascade of a short-term and a long-term linear predictor. These two predictors are usually found in a sequential and therefore suboptimal approach. In this paper we propose an analysis model that jointly finds the two predictors by adding a regularization term in the minimization process to impose sparsity constraints on a high order predictor. The result is a linear predictor that can be easily factorized into the short-term and long-term predictors. This estimation method is then incorporated into an Algebraic Code Excited Linear Prediction scheme and shows to have a better performance than traditional cascade methods and other joint optimization methods, offering lower distortion and higher perceptual speech quality.