Gaussian Mixture Kalman predictive coding of lsfs
(2008) IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008 p.4777-4780- Abstract
- Gaussian Mixture Model (GMM)-based predictive coding of line spectral frequencies (lsf's) 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 optimize each of these linear predictive transform coders using Kalman predictive coding techniques 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 superior coding of lsfs in comparison with the baseline GMM predictive coder. Moreover, we show how running the Kalman predictive coders to convergence can be used to design a stationary predictive... (More)
- Gaussian Mixture Model (GMM)-based predictive coding of line spectral frequencies (lsf's) 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 optimize each of these linear predictive transform coders using Kalman predictive coding techniques 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 superior coding of lsfs in comparison with the baseline GMM predictive coder. Moreover, we show how running the Kalman predictive coders to convergence can be used to design a stationary predictive coding system which again provides superior coding of lsfs but now with no increase in run-time complexity over the baseline. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4092489
- author
- Subasingha, Shaminda ; Murthi, Manohar N. and Andersen, Sören Vang LU
- publishing date
- 2008
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- speech coding, vector quantization, Kalman filtering, Gaussian Mixture, Models
- host publication
- 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12
- pages
- 4777 - 4780
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008
- conference location
- Las Vegas, NV, United States
- conference dates
- 2008-03-31 - 2008-04-04
- external identifiers
-
- wos:000257456703178
- scopus:51449107656
- ISSN
- 1520-6149
- DOI
- 10.1109/ICASSP.2008.4518725
- language
- English
- LU publication?
- no
- id
- 17169146-ac70-4c6c-b88f-85d1eb5b7b6e (old id 4092489)
- date added to LUP
- 2016-04-01 13:32:19
- date last changed
- 2022-01-27 19:42:02
@inproceedings{17169146-ac70-4c6c-b88f-85d1eb5b7b6e, abstract = {{Gaussian Mixture Model (GMM)-based predictive coding of line spectral frequencies (lsf's) 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 optimize each of these linear predictive transform coders using Kalman predictive coding techniques 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 superior coding of lsfs in comparison with the baseline GMM predictive coder. Moreover, we show how running the Kalman predictive coders to convergence can be used to design a stationary predictive coding system which again provides superior coding of lsfs but now with no increase in run-time complexity over the baseline.}}, author = {{Subasingha, Shaminda and Murthi, Manohar N. and Andersen, Sören Vang}}, booktitle = {{2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12}}, issn = {{1520-6149}}, keywords = {{speech coding; vector quantization; Kalman filtering; Gaussian Mixture; Models}}, language = {{eng}}, pages = {{4777--4780}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Gaussian Mixture Kalman predictive coding of lsfs}}, url = {{http://dx.doi.org/10.1109/ICASSP.2008.4518725}}, doi = {{10.1109/ICASSP.2008.4518725}}, year = {{2008}}, }