  We present a deep convolutional GAN which leverages techniques from
MP3/Vorbis audio compression to produce long, high-quality audio samples with
long-range coherence. The model uses a Modified Discrete Cosine Transform
(MDCT) data representation, which includes all phase information. Phase
generation is hence integral part of the model. We leverage the auditory
masking and psychoacoustic perception limit of the human ear to widen the true
distribution and stabilize the training process. The model architecture is a
deep 2D convolutional network, where each subsequent generator model block
increases the resolution along the time axis and adds a higher octave along the
frequency axis. The deeper layers are connected with all parts of the output
and have the context of the full track. This enables generation of samples
which exhibit long-range coherence. We use MP3net to create 95s stereo tracks
with a 22kHz sample rate after training for 250h on a single Cloud TPUv2. An
additional benefit of the CNN-based model architecture is that generation of
new songs is almost instantaneous.
