Ordered vector quantization for neural network pattern classification

The accurate classification of time sequences of vectors is a common goal in signal processing. Vector quantization (VQ) has commonly been used to help encode vectors for subsequent classification. The authors depart from this past approach proposing the use of VQ codebook indices, as opposed to codebook vectors. It is shown that one-dimensional ordering of these indices markedly improves the neural-network-based classification accuracy of acoustic time-frequency patterns. The needs for and extensions of multidimensional codebook indices are described