A KALMAN FILTERING APPROACH TO GMM PREDICTIVE CODING OF LSFS FOR PACKET LOSS CONDITIONS
(2009) 16th International Conference on Digital Signal Processing, 2009 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4092480
- 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, GMM, vector quantization, Kalman filtering
- host publication
- 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
- conference location
- Santorini, Greece
- conference dates
- 2009-07-05 - 2009-07-07
- external identifiers
-
- wos:000276494500073
- scopus:70449586857
- language
- English
- LU publication?
- no
- id
- d79c09ba-36a6-4168-b5ea-763220f6d4a4 (old id 4092480)
- date added to LUP
- 2016-04-04 10:55:48
- date last changed
- 2022-01-29 21:03:56
@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}}, keywords = {{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}}, }