Article (Scientific journals)
Switched adaptive quantiser for speech compression based on optimal companding and correlation
DESPOTOVIC, Vladimir; Peric, Zoran; Velimirovic, Lazar et al.
2011In IET Signal Processing, 5 (7), p. 701-707
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Abstract :
[en] This study describes a novel adaptive quantiser based on the optimal companding technique. Adaptation is achieved by adjusting the input of the fixed or non-adaptive quantiser according to the estimated and quantised gain on each particular frame. In such a way better quantiser adaptation to the varying input statistics is provided. Selection of the appropriate bit rate is performed depending on the value of the correlation coefficient ρ on each frame. The decision thresholds for ρ are determined under the condition that the signal to quantisation noise ratio does not drop under 34.3ρdB, satisfying the G.712 standard quality of speech, while decreasing the bit rate. The information about the gain and about the chosen bit rate is then transferred as a side information to a decoder. Although this slightly increases the side information, the overall savings in the bit rate have shown to be substantial. Theoretical and experimental results are provided, which point out the benefits that can be achieved using the proposed algorithm.
Disciplines :
Computer science
Author, co-author :
DESPOTOVIC, Vladimir ;  University of Belgrade > Technical Faculty in Bor
Peric, Zoran;  University of Nis > Faculty of Electronic Engineering
Velimirovic, Lazar;  Faculty of Electronic Engineering
Delic, Vlado;  University of Novi Sad > Faculty of Technical Sciences
External co-authors :
yes
Language :
English
Title :
Switched adaptive quantiser for speech compression based on optimal companding and correlation
Publication date :
November 2011
Journal title :
IET Signal Processing
ISSN :
1751-9675
eISSN :
1751-9683
Publisher :
Institution of Engineering and Technology, United Kingdom
Volume :
5
Issue :
7
Pages :
701-707
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBilu :
since 11 November 2019

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