IMPLEMENTING THE ADAPTIVE VECTOR QUANTIZER USING CARPENTER/GROSSBERG NET

AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vectorquantizer with a fixed codebook trained and generated by the LBG or other algorithms by applyinga variab|e codebook.In this paper,we describe an effective and efficient implementation of AVQby modifying the CCN(Carpenter/Grossberg Net).The encoding process of AVQ is very similarto the learning process of the CGN.We study several different encoding schemes,includingwaveform AVQ,analysed parameter AVQ and so on,implemented by the CGN.And we simulatethe encoding performance of each scheme for encoding Gaussian process source,first order Gauss-Markov process source and practical speech signal.Our simulation results show that good qualityboth in subjective and objective tests can be obtained in a low or middle bit rate range.

Implementing the adaptive vector quantizer using carpenter/grossberg net

AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vector quantizer with a fixed codebook trained and generated by the LBG or other algorithms by applying a variab|e codebook.In this paper,we describe an effective and efficient implementation of AVQ by modifying the CCN(Carpenter/Grossberg Net).The encoding process of AVQ is very similar to the learning process of the CGN.We study several different encoding schemes,including waveform AVQ,analysed parameter AVQ and so on,implemented by the CGN.And we simulate the encoding performance of each scheme for encoding Gaussian process source,first order Gauss- Markov process source and practical speech signal.Our simulation results show that good quality both in subjective and objective tests can be obtained in a low or middle bit rate range.