Abstract
Gaussian Mixture Model (GMM)-based Kalman predictive coders have been shown to perform better than baselineGMM 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.
| Original language | English |
|---|---|
| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Pages | 4105-4108 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Sep 23 2009 |
| Event | 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China Duration: Apr 19 2009 → Apr 24 2009 |
Other
| Other | 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taipei |
| Period | 4/19/09 → 4/24/09 |
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Keywords
- GMM
- Kalman filtering
- Speech coding
- Vector quantization
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering
Cite this
On GMM Kalman predictive coding of LSFS for packet loss. / Subasingha, Shaminda; Murthi, Manohar; Andersen, Søren Vang.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2009. p. 4105-4108 4960531.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - On GMM Kalman predictive coding of LSFS for packet loss
AU - Subasingha, Shaminda
AU - Murthi, Manohar
AU - Andersen, Søren Vang
PY - 2009/9/23
Y1 - 2009/9/23
N2 - Gaussian Mixture Model (GMM)-based Kalman predictive coders have been shown to perform better than baselineGMM 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.
AB - Gaussian Mixture Model (GMM)-based Kalman predictive coders have been shown to perform better than baselineGMM 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.
KW - GMM
KW - Kalman filtering
KW - Speech coding
KW - Vector quantization
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UR - http://www.scopus.com/inward/citedby.url?scp=70349202234&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2009.4960531
DO - 10.1109/ICASSP.2009.4960531
M3 - Conference contribution
SN - 9781424423545
SP - 4105
EP - 4108
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -