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ON GMM KALMAN PREDICTIVE CODING OF LSFS FOR PACKET LOSS

Subasingha, Shaminda; Murthi, Manohar N. and Andersen, Sören Vang LU (2009) IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 In 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS p.4105-4108
Abstract
Gaussian Mixture Model (GMM)-based Kalman predictive coders have been shown to perform better than baseline GMM Recursive Coders in predictive coding of Line Spectral Frequencies (LSFs) for both clean and packet loss conditions However, these stationary GMM Kalman predictive coders were not specifically designed for operation in packet loss conditions. In this paper, we demonstrate an approach to the the design of GMM-based predictive coding for packet loss channels. In particular, we show how a stationary GMM Kalman predictive coder can be modified to obtain a set of encoding and decoding modes, each with different Kalman gains. This approach leads to more robust performance of predictive coding of LSFs in packet loss conditions, as the... (More)
Gaussian Mixture Model (GMM)-based Kalman predictive coders have been shown to perform better than baseline GMM Recursive Coders in predictive coding of Line Spectral Frequencies (LSFs) for both clean and packet loss conditions However, these stationary GMM Kalman predictive coders were not specifically designed for operation in packet loss conditions. In this paper, we demonstrate an approach to the the design of GMM-based predictive coding for packet loss channels. In particular, we show how a stationary GMM Kalman predictive coder can be modified to obtain a set of encoding and decoding modes, each with different Kalman gains. This approach leads to more robust performance of predictive coding of LSFs in packet loss conditions, as the coder mismatch between the encoder and decoder are minimized. Simulation results show that this Robust GMM Kalman predictive coder performs better than other baseline GMM predictive coders with no increase in complexity. To the best of our knowledge, no previous work has specifically examined the design of GMM predictive coders for packet loss conditions. (Less)
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author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
speech coding, Kalman filtering, GMM, vector quantization
in
2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS
pages
4105 - 4108
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
external identifiers
  • wos:000268919202101
  • scopus:70349202234
ISSN
1520-6149
DOI
10.1109/ICASSP.2009.4960531
language
English
LU publication?
no
id
19015880-c596-4192-8809-e8ae4931a438 (old id 4092484)
date added to LUP
2013-10-17 10:56:16
date last changed
2017-06-25 03:50:42
@inproceedings{19015880-c596-4192-8809-e8ae4931a438,
  abstract     = {Gaussian Mixture Model (GMM)-based Kalman predictive coders have been shown to perform better than baseline GMM Recursive Coders in predictive coding of Line Spectral Frequencies (LSFs) for both clean and packet loss conditions However, these stationary GMM Kalman predictive coders were not specifically designed for operation in packet loss conditions. In this paper, we demonstrate an approach to the the design of GMM-based predictive coding for packet loss channels. In particular, we show how a stationary GMM Kalman predictive coder can be modified to obtain a set of encoding and decoding modes, each with different Kalman gains. This approach leads to more robust performance of predictive coding of LSFs in packet loss conditions, as the coder mismatch between the encoder and decoder are minimized. Simulation results show that this Robust GMM Kalman predictive coder performs better than other baseline GMM predictive coders with no increase in complexity. To the best of our knowledge, no previous work has specifically examined the design of GMM predictive coders for packet loss conditions.},
  author       = {Subasingha, Shaminda and Murthi, Manohar N. and Andersen, Sören Vang},
  booktitle    = {2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS},
  issn         = {1520-6149},
  keyword      = {speech coding,Kalman filtering,GMM,vector quantization},
  language     = {eng},
  pages        = {4105--4108},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {ON GMM KALMAN PREDICTIVE CODING OF LSFS FOR PACKET LOSS},
  url          = {http://dx.doi.org/10.1109/ICASSP.2009.4960531},
  year         = {2009},
}