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.