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.

本文提出了一种基于序列生成对抗网络的应用方法 (SeqGAN)，用于创建和捕获音符和和弦的多声部音乐序列的表征，同时利用神经网络从该嵌入式音乐词空间的模拟分布中学习预测序列的分布，实验表明该方法可以稳定地训练 GAN 并创造出具有音乐连贯性且在定量和定性方面均有所提升的音乐序列。