An adaptive neural network VQ algorithm and its implementation on the DSP32C signal processor

T. Fjallbrant,F. Mekuria

doi:10.1109/iscas.1994.409621

An adaptive neural network VQ algorithm and its implementation on the DSP32C signal processor

T. Fjallbrant, F. Mekuria

https://doi.org/10.1109/iscas.1994.409621

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Publication Date: May 30, 1994

Affiliation: Linköping University

#Vector Quantization Algorithm #Adaptive Vector Quantization Algorithm + Show 8 more

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Abstract

In this paper the implementation of an adaptive vector quantization (VQ) algorithm is described based on a computation and memory efficient neural network model described in previous publications. The adaptive neural VQ (ANVQ) algorithm is based on the full search VQ, and utilizes the modified frequency sensitive competitive learning algorithm for training the codebook. The ANVQ algorithm improves the codebook search speed and utilization and gives better output SNR compared to conventionally designed VQ algorithms, and for signals not included in the codebook training-sequence. Furthermore, the ANVQ model architecture is very suitable for VLSI implementation. A C++ interface to the DSP32C signal processor is used for implementation of the ANVQ algorithm. >

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