EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template

TL;DR

EMelodyGen is a system that generates emotionally expressive melodies in ABC notation by using a musical feature template, leveraging a large annotated dataset and psychological insights to control emotional expression.

Contribution

It introduces a novel template-based approach for emotion-conditioned melody generation and creates a large emotional music dataset with data augmentation.

Findings

Achieved 99% music21 parsing rate.

Generated melodies aligned with emotional expressions in 91% of blind tests.

Validated the effectiveness of feature controls through ablation studies.

Abstract

The EMelodyGen system focuses on emotional melody generation in ABC notation controlled by the musical feature template. Owing to the scarcity of well-structured and emotionally labeled sheet music, we designed a template for controlling emotional melody generation by statistical correlations between musical features and emotion labels derived from small-scale emotional symbolic music datasets and music psychology conclusions. We then automatically annotated a large, well-structured sheet music collection with rough emotional labels by the template, converted them into ABC notation, and reduced label imbalance by data augmentation, resulting in a dataset named Rough4Q. Our system backbone pre-trained on Rough4Q can achieve up to 99% music21 parsing rate and melodies generated by our template can lead to a 91% alignment on emotional expressions in blind listening tests. Ablation studies further validated the effectiveness of the feature controls in the template. Available code and demos are at https://github.com/monetjoe/EMelodyGen.