Proceedings Article10.1109/ICASSP.1986.1169055
Low-rate speech encoding using vector quantization and subband coding
Huseyin Abut,S. Ergezinger +1 more
- 07 Apr 1986
- Vol. 11, pp 449-452
120
TL;DR: The best quality and the lowest transmission rates were achieved by a residually excited subband coded vector quantization system coupled with a three-way classifier with design parameters: LPC order P=14; 32-band complete binary tree QMF filter bank implementation of SBC and VQ waveform encoding with dimension K=32 at an overall bitrate of 3,100 bps.
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Abstract: Vector quantization (VQ), subband coding (SBC) and linear predictive coding (LPC) are three of the most effective data compression schemes used for medium-to-narrow band speech coding. In this study, we have attempted to improve the quality of encoded speech by using various combinations of these three coding methods. Waveform coders with rates 2400-9600 bits per second resulted in overall signal-to-distortion ratios of 6-12 dB. We have obtained somewhat lower values for segmented SNR's in the case of straight waveform encoding and higher values for the residually excited subband coded VQ quantizers as expected. However, informal listening tests yielded noticeable improvements over those of straight waveform VQ results. The best quality and the lowest transmission rates were achieved by a residually excited subband coded vector quantization system coupled with a three-way classifier with design parameters: LPC order P=14; 32-band complete binary tree QMF filter bank implementation of SBC and VQ waveform encoding with dimension K=32 at an overall bitrate of 3,100 bps.
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Vector quantization: A pattern-matching technique for speech coding
Allen Gersho,Vladimir Cuperman +1 more
TL;DR: Recent results obtained in waveform coding of speech with vector quantization are reviewed, with Vector quantization appearing to be a suitable coding technique which caters to this dual requirement of effective speech coding.
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The Design of Trellis Waveform Coders
TL;DR: The mare algorithm uses a training sequence of actual data from a source to improve an initial trellis decoder and an additional algorithm extends the constraint length of a given decoder to allow the automatic design of a Trellis encoding system for a particular source.
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