A Kalman filtering approach to GMM predictive coding of LSFS for packet loss conditions

Shaminda Subasingha, Manohar N. Murthi, Søren Vang Andersen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gaussian Mixture Model (GMM)-based vector quantization of Line Spectral Frequencies (LSFs) has gained wide acceptance in speech coding. In predictive coding of LSFs, the GMM approach utilizing Kalman filtering principles to account for quantization noise has been shown to perform better than a baseline GMM Recursive Coder approaches for both clean and packet loss conditions at roughly the same complexity However, the GMM Kalman based predictive coder was not specifically designed for operation in packet loss conditions. In this paper, we show how an initial GMM Kalman predictive coder can be utilized to obtain a robust GMM predictive coder specifically designed to operate in packet loss.In particular, we demonstrate how one can define sets of encoding and decoding modes, and design special Kalman encoding and decoding gains for each set. With this framework,GMM predictive coding design can be viewed as determining the special Kalman gains that minimize the expected minimum mean squared error at the decoder in packet loss conditions.The simulation results demonstrate that the proposed robust Kalman predictive coder achieves better performance than the baseline GMM predictive coders.

Original languageEnglish (US)
Title of host publicationDSP 2009:16th International Conference on Digital Signal Processing, Proceedings
DOIs
StatePublished - Nov 20 2009
EventDSP 2009:16th International Conference on Digital Signal Processing - Santorini, Greece
Duration: Jul 5 2009Jul 7 2009

Publication series

NameDSP 2009: 16th International Conference on Digital Signal Processing, Proceedings

Other

OtherDSP 2009:16th International Conference on Digital Signal Processing
CountryGreece
CitySantorini
Period7/5/097/7/09

Keywords

  • GMM
  • Kalman filtering
  • Speech coding
  • Vector quantization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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