Music creation involves not only composing the different parts (e.g., melody,
chords) of a musical work but also arranging/selecting the instruments to play
the different parts. While the former has received increasing attention, the
latter has not been much investigated. This paper presents, to the best of our
knowledge, the first deep learning models for rearranging music of arbitrary
genres. Specifically, we build encoders and decoders that take a piece of
polyphonic musical audio as input and predict as output its musical score. We
investigate disentanglement techniques such as adversarial training to separate
latent factors that are related to the musical content (pitch) of different
parts of the piece, and that are related to the instrumentation (timbre) of the
parts per short-time segment. By disentangling pitch and timbre, our models
have an idea of how each piece was composed and arranged. Moreover, the models
can realize "composition style transfer" by rearranging a musical piece without
much affecting its pitch content. We validate the effectiveness of the models
by experiments on instrument activity detection and composition style transfer.
To facilitate follow-up research, we open source our code at
this https URL

本文介绍一种基于深度学习的音乐曲目排列模型，通过对乐曲音频的编码和解码预测其乐谱，并使用解缠技术将其中代表音乐内容和乐器的因素分离，实现曲式转换。