High frequency reconstruction using neural network system

A decoder system comprising:

• a demuxer for separating a bitstream into an encoded low-band audio signal representation and a set of high frequency reconstruction, HFR, parameters;
• a decoder for decoding the encoded low-band audio signal representation to provide a low-band audio signal in a filter bank domain;
• a generative model for reconstructing a filter bank domain high-band signal using a neural network system trained to predict samples of a high-band audio signal in the filter bank domain given samples of the filter bank domain low-band signal and said HFR parameters;
• the neural network system being configured to autoregressively generate a current sample (xm) for a current time slot (m) of the filter bank domain high-band signal, the current sample including a plurality of values, each corresponding to a channel of the filter bank, the system comprising:
• a processing layer, trained to generate conditioning information for the current sample based on quantized samples of the filter bank domain low-band signal and said HFR parameters, and
• an output layer subdivided into a plurality of sequentially executed sub-layers, each sub-layer being trained to generate a subset of values of the current sample, given the conditioning information from the processing layer and on samples generated by any previously executed sub-layers;
• and
• a synthesis filter bank for synthesizing a time domain audio signal from the filter bank domain low-band signal and the reconstructed filter bank domain high-band signal.

A neural network system for autoregressively generating a current sample (xm) for a current time slot (m) of a filter bank representation of an audio signal, the current sample including a plurality of values, each corresponding to a channel of the filter bank, the system comprising:

• a first and a second submodel, each submodel including:
• a processing layer, trained to generate conditioning information for the current sample, and
• an output layer subdivided into a plurality of sequentially executed sub-layers, each sub-layer being trained to generate a subset of values of the current sample, given the conditioning information from the processing layer and on samples generated by any previously executed sub-layers,
• wherein the first submodel is trained to generate values of the current sample corresponding to a low-band frequency range, given previously generated samples of the filter bank representation and conditioned by quantized samples of the filter bank representation, and
• wherein the second submodel is trained to generate values of the current sample corresponding to a high-band frequency range, given previously generated samples of the filter bank representation and conditioned by quantized samples of the filter bank representation and by a set of high frequency reconstruction parameters.