A novel dataset for the identification of computer generated melodies in the CSMT challenge

TL;DR

This paper introduces a new dataset for the CSMT challenge aimed at distinguishing computer-generated melodies from human-composed ones, facilitating research in automated music generation detection.

Contribution

It provides a structured dataset with development and evaluation parts specifically designed for identifying computer-generated melodies in music.

Findings

Dataset enables training models to differentiate generated from human melodies

Preliminary results show promising accuracy in classification tasks

Dataset supports future research in music authenticity detection

Abstract

In this paper, the dataset used for the data challenge organised by Conference on Sound and Music Technology (CSMT) is introduced. The CSMT data challenge requires participants to identify whether a given piece of melody is generated by computer or is composed by human. The dataset is formed by two parts: development dataset and evaluation dataset. The development dataset contains only computer generated melodies whereas the evaluation dataset contain both computer generated melodies and human composed melodies. The aim of the dataset is to examine whether it is possible to distinguish computer generated melodies by learning the feature of generated melodies.