  Most existing neural network models for music generation use recurrent neural
networks. However, the recent WaveNet model proposed by DeepMind shows that
convolutional neural networks (CNNs) can also generate realistic musical
waveforms in the audio domain. Following this light, we investigate using CNNs
for generating melody (a series of MIDI notes) one bar after another in the
symbolic domain. In addition to the generator, we use a discriminator to learn
the distributions of melodies, making it a generative adversarial network
(GAN). Moreover, we propose a novel conditional mechanism to exploit available
prior knowledge, so that the model can generate melodies either from scratch,
by following a chord sequence, or by conditioning on the melody of previous
bars (e.g. a priming melody), among other possibilities. The resulting model,
named MidiNet, can be expanded to generate music with multiple MIDI channels
(i.e. tracks). We conduct a user study to compare the melody of eight-bar long
generated by MidiNet and by Google's MelodyRNN models, each time using the same
priming melody. Result shows that MidiNet performs comparably with MelodyRNN
models in being realistic and pleasant to listen to, yet MidiNet's melodies are
reported to be much more interesting.

Sound;Artificial Intelligence