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We propose a neural audio generative model, MDCTNet, operating in theperceptually weighted domain of an adaptive modified discrete cosine transform(MDCT). The architecture of the model captures correlations in both time andfrequency directions with recurrent layers (RNNs). An audio coding system isobtained by training MDCTNet on a diverse set of fullband monophonic audiosignals at 48 kHz sampling, conditioned by a perceptual audio encoder. In asubjective listening test with ten excerpts chosen to be balanced acrosscontent types, yet stressful for both codecs, the mean performance of theproposed system for 24 kb/s variable bitrate (VBR) is similar to that of Opusat twice the bitrate.

High Quality Audio Coding with MDCTNet