ON GMM KALMAN PREDICTIVE CODING OF LSFS FOR PACKET LOSS
(2009) IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4092484
- author
- Subasingha, Shaminda ; Murthi, Manohar N. and Andersen, Sören Vang LU
- publishing date
- 2009
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- speech coding, Kalman filtering, GMM, vector quantization
- host publication
- 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
- conference location
- Taipei, Taiwan
- conference dates
- 2009-04-19 - 2009-04-24
- 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
- 2016-04-01 13:00:11
- date last changed
- 2022-04-21 19:12:20
@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}}, keywords = {{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}}, doi = {{10.1109/ICASSP.2009.4960531}}, year = {{2009}}, }