NDLI: The correlated noise reducing model using a kalman filter for speech vector quantization

Content Provider	IEEE Xplore Digital Library
Author	Rassameyoungtong, J. Srinonchat, J.
Copyright Year	2012
Description	Author affiliation: Department of Electronics and Telecommunication Engineering, Rajamangala University of Technology, Thanyaburi 39 moo1, Rangsit-Nakorn Nayok Road, Klong Hok, Rathumtani 12110, Thailand (Rassameyoungtong, J.; Srinonchat, J.)
Abstract	The kalman filter is a recursive predictive filter that is based on the use of state space techniques and recursive algorithms. The advantage of kalman filter is, it estimates the state of dynamic system which can be disturbed by some noise. Thus this article presents the correlated noise reducing model using a kalman filter for speech vector quantization. The Q and R covariance constant parameters are investigated to provide the optimal performance with minimum noise of speech vector quantization signal. The results show that this model provides the minimum error as 1.0023 and 0.3622 for measurement error covariance and estimatation error covariance respectively.
Starting Page	1
Ending Page	4
File Size	165120
Page Count	4
File Format	PDF
ISBN	9781467356947
e-ISBN	9781467356961
DOI	10.1109/EDSSC.2012.6482849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-03
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Noise Equations Speech Measurement uncertainty Mathematical model Noise measurement recursive algorithms kamal filter noise reducition Speech processing covariance constant
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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