Speech analysis and synthesis methods developed at ECL in NTT-From LPC to LSP-
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Communications of the ACM - Special issue on digital multimedia systems
Vector quantization and signal compression
Vector quantization and signal compression
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Introduction to data compression
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Efficient vector quantization of LPC parameters at 24 bits/frame
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
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IEEE Transactions on Information Theory
High-resolution quantization theory and the vector quantizer advantage
IEEE Transactions on Information Theory
Asymptotically optimal block quantization
IEEE Transactions on Information Theory
Least squares quantization in PCM
IEEE Transactions on Information Theory
Digital Signal Processing
Optimum switched split vector quantization of LSF parameters
Signal Processing
A new hybrid vector quantizer of LSF parameters
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
Reduced complexity two stage vector quantization
Digital Signal Processing
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In this article, we first review the vector quantiser and discuss its well-known advantages over the scalar quantiser, namely the space-filling advantage, the shape advantage, and the memory advantage. It is important to understand why vector quantisers always perform better than any other quantisation scheme for a given dimension, as this will provide the basis for our investigation on improving product code vector quantisers which, despite having much lower computational and memory requirements, result in suboptimal quantisation performance. The main focus is on improving the efficiency of the split vector quantiser (SVQ), in terms of computational complexity and rate-distortion performance. Though SVQ has lower computational and memory requirements than those of the unconstrained vector quantiser, the vector splitting process adds numerous constraints to the codebook, which results in suboptimal quantisation performance. Specifically, the reduced dimensionality affects all three vector quantiser advantages. Therefore, we investigate a new type of hybrid vector quantiser, called the switched split vector quantiser (SSVQ), that addresses the memory and shape suboptimality of SVQ, leading to better quantisation performance. In addition, the SSVQ has lower computational complexity than the SVQ, at the expense of higher memory requirements for storing the codebooks. We evaluate the performance of SSVQ in LPC parameter quantisation, used in narrowband CELP speech coders, and compare it against other quantisation schemes.