Automatic Melody Reduction via Shortest Path Finding

TL;DR

This paper introduces a graph-based shortest path algorithm for automatic melody reduction that improves musical coherence and fidelity across various genres, enhancing music analysis and generation tasks.

Contribution

It presents a novel, simple computational approach for melody reduction using shortest path finding, applicable to multiple genres and outperforming existing methods.

Findings

Produces more faithful melody reductions

Generates more musically coherent results

Outperforms state-of-the-art style transfer methods

Abstract

Melody reduction, as an abstract representation of musical compositions, serves not only as a tool for music analysis but also as an intermediate representation for structured music generation. Prior computational theories, such as the Generative Theory of Tonal Music, provide insightful interpretations of music, but they are not fully automatic and usually limited to the classical genre. In this paper, we propose a novel and conceptually simple computational method for melody reduction using a graph-based representation inspired by principles from computational music theories, where the reduction process is formulated as finding the shortest path. We evaluate our algorithm on pop, folk, and classical genres, and experimental results show that the algorithm produces melody reductions that are more faithful to the original melody and more musically coherent than other common melody downsampling methods. As a downstream task, we use melody reductions to generate symbolic music variations. Experiments show that our method achieves higher quality than state-of-the-art style transfer methods.