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A KALMAN FILTERING APPROACH TO GMM PREDICTIVE CODING OF LSFS FOR PACKET LOSS CONDITIONS

Subasingha, Shaminda; Murthi, Manohar N. and Andersen, Sören Vang LU (2009) 16th International Conference on Digital Signal Processing, 2009 In 2009 16TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING, VOLS 1 AND 2 p.434-439
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... (More)
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. (Less)
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author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
speech coding, GMM, vector quantization, Kalman filtering
in
2009 16TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING, VOLS 1 AND 2
pages
434 - 439
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
16th International Conference on Digital Signal Processing, 2009
external identifiers
  • WOS:000276494500073
  • Scopus:70449586857
language
English
LU publication?
no
id
d79c09ba-36a6-4168-b5ea-763220f6d4a4 (old id 4092480)
date added to LUP
2013-10-17 10:56:18
date last changed
2017-01-01 08:00:29
@inproceedings{d79c09ba-36a6-4168-b5ea-763220f6d4a4,
  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.},
  author       = {Subasingha, Shaminda and Murthi, Manohar N. and Andersen, Sören Vang},
  booktitle    = {2009 16TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING, VOLS 1 AND 2},
  keyword      = {speech coding,GMM,vector quantization,Kalman filtering},
  language     = {eng},
  pages        = {434--439},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {A KALMAN FILTERING APPROACH TO GMM PREDICTIVE CODING OF LSFS FOR PACKET LOSS CONDITIONS},
  year         = {2009},
}