GMM-Based Iterative Entropy Coding for Spectral Envelopes of Speech and Audio

Abstract

Spectral envelope modelling is a central part of speech and audio codecs and is traditionally based on either vector quantization or scalar quantization followed by entropy coding. To bridge the coding performance of vector quantization with the low complexity of the scalar case, we propose an iterative approach for entropy coding the spectral envelope parameters. For each parameter, a univariate probability distribution is derived from a Gaussian mixture model of the joint distribution and the previously quantized parameters used as a-priori information. Parameters are then iteratively and individually scalar quantized and entropy coded. Unlike vector quantization, the complexity of proposed method does not increase exponentially with dimension and bitrate. Moreover, the coding resolution and dimension can be adaptively modified without retraining the model. Experimental results show that these important advantages do not impair coding efficiency compared to a state-of-art vector quantization scheme.