We propose a neural audio generative model, MDCTNet, operating in the
perceptually weighted domain of an adaptive modified discrete cosine transform
(MDCT). The architecture of the model captures correlations in both time and
frequency directions with recurrent layers (RNNs). An audio coding system is
obtained by training MDCTNet on a diverse set of fullband monophonic audio
signals at 48 kHz sampling, conditioned by a perceptual audio encoder. In a
subjective listening test with ten excerpts chosen to be balanced across
content types, yet stressful for both codecs, the mean performance of the
proposed system for 24 kb/s variable bitrate (VBR) is similar to that of Opus
at twice the bitrate.Comment: Five pages, five figure