@article{MAB988C5E,
title = "Polyphonic Music Generation with Sequence Generative Adversarial Networks",
journal = "Journal of KIISE, JOK",
year = "2024",
issn = "2383-630X",
doi = "10.5626/JOK.2024.51.1.78",
author = "Sang-gil Lee,Uiwon Hwang,Seonwoo Min,Sungroh Yoon",
keywords = "music generation,generative adversarial network,reinforcement learning,deep generative model,deep learning",
abstract = "In this paper, we propose an application of sequence generative adversarial networks (SeqGAN) for generating polyphonic musical sequences. We introduce a representation of polyphonic MIDI files that could encapsulate both chords and melodies with dynamic timings. This method condensed the duration, octaves, and keys of both melodies and chords into a single word vector representation. Our generator composed of recurrent neural networks was trained to predict distributions of musical word sequences. Additionally, we employed the least square loss function for the discriminator to stabilize training of the model. Our model could create sequences that are musically coherent. It exhibited improved quantitative and qualitative measures."
}