Adaptive block transform coding of speech based on LPC vector quantization

摘要

The authors describe several adaptive block transform speech coding systems based on vector quantization of linear predictive coding (LPC) parameters. Specifically, the authors vector quantize the LPC parameters (LPCVQ) associated with each speech block and transmit the index of the code vector as overhead information. This code vector will determine the short-term spectrum of the block and, in turn, can be used for optimal bit allocation among the transform coefficients. In order to get a better estimate of the speech spectrum, the authors also consider the possibility of incorporating pitch information in the coder. In addition, entropy-coded zero-memory quantization of the transform coefficients is considered as an alternative to Lloyd-Max quantization. An adaptive BTC scheme based on LPCVQ and using entropy-coded quantizers is developed. Extensive simulations are used to evaluate the performance of this scheme.