Presents novel sequential and parallel learning techniques for codebook design in vector quantizers using neural network approaches. These techniques are used in the training phase of the vector quantizer design. These learning techniques combine the split-and-cluster methodology of the traditional vector quantizer design with neural learning, and lead to better quantizer design (with fewer distortions). The sequential learning approach overcomes the code word underutilization problem of the competitive learning network. As a result, this network only requires partial or zero updating, as opposed to full neighbor updating as needed in the self organizing feature map. The parallel learning network, while satisfying the above characteristics, also leads to parallel learning of the codewords. The parallel learning technique can be used for faster codebook design in a multiprocessor environment. It is shown that this sequential learning scheme can sometimes outperform the traditional LBG algorithm, while the parallel learning scheme performs very close to the LGB and the sequential learning algorithms.>

Many techniques for quantizing large sets of input vectors into much smaller sets of output vectors have been developed. Various neural network based techniques for generating the input vectors via system training are studied. The variations are centered around a neural net vector quantization (NNVQ) method which combines the well-known conventional Linde, Buzo and Gray (1980) (LBG) technique and the neural net based Kohonen (1984) technique. Sequential and parallel learning techniques for designing efficient NNVQs are given. The schemes presented require less computation time due to a new modified gain formula, partial/zero neighbor updating, and parallel learning of the code vectors. Using Gaussian-Markov source and speech signal benchmarks, it is shown that these new approaches lead to distortion as good as or better than that obtained using the LBG and Kohonen approaches.>

The authors present results of a comparative study of the efficiency of neural-net based approaches (Kohonen and NNVQ) and conventional (LBG and K-means) approaches for vector quantization. They focus on the accuracy and speed of the four methods for the VQ (vector quantization) design problem using two different input sources: a Gaussian Markov source and a speech signal (digit strings). It is shown that the LBG (Y. Linde, A. Buzo, and R. M. Gray 1980) and NNVQ methods achieve more accurate vector quantization than the K-means and Kohonen methods. The NNVQ method offers computational advantages, since the neural-net-based algorithm can be implemented with the use of parallel processors due to its inherent parallelism.>

A two-stage predictor is proposed. It is capable of predicting ARMA processes accurately with a reduced number of parameters. This is achieved by cascading a classical linear predictor, where the constraint on the predictor order is relaxed, and a pole-zero recursive like structure. Adaptive algorithms and sample results are given to demonstrate the excellent performance of the proposed technique.