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Gaussian Mixture Kalman Predictive Coding of Line Spectral Frequencies

Subasingha, Shaminda; Murthi, Manohar N. and Andersen, Sören Vang LU (2009) In IEEE Transactions on Audio, Speech, and Language Processing 17(2). p.379-391
Abstract
Gaussian mixture model (GMM)-based predictive coding of line spectral frequencies (LSFs) has gained wide acceptance. In such coders, each mixture of a GMM can be interpreted as defining a linear predictive transform coder. In this paper, we use Kalman filtering principles to model each of these linear predictive transform coders to present GMM Kalman predictive coding. In particular, we show how suitable modeling of quantization noise leads to an adaptive a posteriori GMM that defines a signal-adaptive predictive coder that provides improved coding of LSFs in comparison with the baseline recursive GMM predictive coder. Moreover, we show how running the GMM Kalman predictive coders to convergence can be used to design a stationary GMM... (More)
Gaussian mixture model (GMM)-based predictive coding of line spectral frequencies (LSFs) has gained wide acceptance. In such coders, each mixture of a GMM can be interpreted as defining a linear predictive transform coder. In this paper, we use Kalman filtering principles to model each of these linear predictive transform coders to present GMM Kalman predictive coding. In particular, we show how suitable modeling of quantization noise leads to an adaptive a posteriori GMM that defines a signal-adaptive predictive coder that provides improved coding of LSFs in comparison with the baseline recursive GMM predictive coder. Moreover, we show how running the GMM Kalman predictive coders to convergence can be used to design a stationary GMM Kalman predictive coding system which again provides improved coding of LSFs but now with only a modest increase in run-time complexity over the baseline. In packet loss conditions, this stationary GMM Kalman predictive coder provides much better performance than the recursive GMM predictive coder, and in fact has comparable mean performance to a memoryless GMM coder. Finally, we illustrate how one can utilize Kalman filtering principles to design a postfilter which enhances decoded vectors from a recursive GMM predictive coder without any modifications to the encoding process. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Gaussian mixture models (GMMs), Kalman filtering, speech coding, vector, quantization (VQ)
in
IEEE Transactions on Audio, Speech, and Language Processing
volume
17
issue
2
pages
379 - 391
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000262933700016
  • scopus:70350451583
ISSN
1558-7924
DOI
10.1109/TASL.2008.2008735
language
English
LU publication?
no
id
08ade6c4-0289-4cdf-8c27-a3f64ae3b299 (old id 4092475)
date added to LUP
2013-10-17 10:55:42
date last changed
2017-01-01 04:35:33
@article{08ade6c4-0289-4cdf-8c27-a3f64ae3b299,
  abstract     = {Gaussian mixture model (GMM)-based predictive coding of line spectral frequencies (LSFs) has gained wide acceptance. In such coders, each mixture of a GMM can be interpreted as defining a linear predictive transform coder. In this paper, we use Kalman filtering principles to model each of these linear predictive transform coders to present GMM Kalman predictive coding. In particular, we show how suitable modeling of quantization noise leads to an adaptive a posteriori GMM that defines a signal-adaptive predictive coder that provides improved coding of LSFs in comparison with the baseline recursive GMM predictive coder. Moreover, we show how running the GMM Kalman predictive coders to convergence can be used to design a stationary GMM Kalman predictive coding system which again provides improved coding of LSFs but now with only a modest increase in run-time complexity over the baseline. In packet loss conditions, this stationary GMM Kalman predictive coder provides much better performance than the recursive GMM predictive coder, and in fact has comparable mean performance to a memoryless GMM coder. Finally, we illustrate how one can utilize Kalman filtering principles to design a postfilter which enhances decoded vectors from a recursive GMM predictive coder without any modifications to the encoding process.},
  author       = {Subasingha, Shaminda and Murthi, Manohar N. and Andersen, Sören Vang},
  issn         = {1558-7924},
  keyword      = {Gaussian mixture models (GMMs),Kalman filtering,speech coding,vector,quantization (VQ)},
  language     = {eng},
  number       = {2},
  pages        = {379--391},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Audio, Speech, and Language Processing},
  title        = {Gaussian Mixture Kalman Predictive Coding of Line Spectral Frequencies},
  url          = {http://dx.doi.org/10.1109/TASL.2008.2008735},
  volume       = {17},
  year         = {2009},
}