Flexible Quantization of Audio and Speech based on the Autoregressive Model

We describe a coding scheme based on audio and speech quantization with an adaptive quantizer derived from the autoregressive model under high-rate assumptions. The main advantage of this scheme compared to state-of-the-art training-based coders is its flexibility. The scheme can adapt in real time to any particular rate and has a computational complexity independent of the rate. Experiments indicate that, compared with a non-scalable conventional fixed-rate code-excited linear predictive (CELP) coding scheme, our real time scalable coder with scalar quantization performs at least as well in the constrained entropy case, and has nearly identical performance for the constrained resolution case.