Music Sentiment Transfer

TL;DR

This paper introduces the novel task of music sentiment transfer, applying sentiment changes to MIDI music using CycleGAN, addressing challenges unique to music's temporal nature and dataset scarcity.

Contribution

It proposes using CycleGAN with cycle consistency loss for music sentiment transfer on symbolic MIDI data, a new approach in this domain.

Findings

Music sentiment transfer is more challenging than image sentiment transfer.

CycleGAN effectively creates content-preserving sentiment modifications.

The task faces difficulties due to music's temporal complexity and limited datasets.

Abstract

Music sentiment transfer is a completely novel task. Sentiment transfer is a natural evolution of the heavily-studied style transfer task, as sentiment transfer is rooted in applying the sentiment of a source to be the new sentiment for a target piece of media; yet compared to style transfer, sentiment transfer has been only scantily studied on images. Music sentiment transfer attempts to apply the high level objective of sentiment transfer to the domain of music. We propose CycleGAN to bridge disparate domains. In order to use the network, we choose to use symbolic, MIDI, data as the music format. Through the use of a cycle consistency loss, we are able to create one-to-one mappings that preserve the content and realism of the source data. Results and literature suggest that the task of music sentiment transfer is more difficult than image sentiment transfer because of the temporal characteristics of music and lack of existing datasets.