  We propose an application of sequence generative adversarial networks
(SeqGAN), which are generative adversarial networks for discrete sequence
generation, for creating polyphonic musical sequences. Instead of a monophonic
melody generation suggested in the original work, we present an efficient
representation of a polyphony MIDI file that simultaneously captures chords and
melodies with dynamic timings. The proposed method condenses duration, octaves,
and keys of both melodies and chords into a single word vector representation,
and recurrent neural networks learn to predict distributions of sequences from
the embedded musical word space. We experiment with the original method and the
least squares method to the discriminator, which is known to stabilize the
training of GANs. The network can create sequences that are musically coherent
and shows an improved quantitative and qualitative measures. We also report
that careful optimization of reinforcement learning signals of the model is
crucial for general application of the model.
