Automatic Melody Reduction via Shortest Path Finding

2025-08-02

AI Summary

This paper addresses the problem of cross-genre automatic melody simplification. We propose a novel graph-structured modeling approach that formalizes melody simplification as a shortest-path optimization problem under musical constraints. Specifically, we construct a weighted directed graph inspired by computational music theory, where nodes represent pitch–duration pairs and edge weights encode musical priors—including intervallic smoothness, rhythmic stability, and tonal coherence—enabling end-to-end, annotation-free melody reduction. To our knowledge, this is the first work to formulate melody simplification as a graph optimization task. Extensive evaluation across pop, folk, and classical genres demonstrates that our method significantly outperforms both rule-based and deep learning baselines in terms of melodic fidelity and musical coherence. Moreover, when extended to structured variation generation, it surpasses state-of-the-art style transfer models in preserving structural integrity while enabling stylistic adaptation.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Constraint Satisfaction and Optimization: Constraint Optimization
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Automatic melody reduction for music analysis and generation
• Graph-based shortest path method for melody simplification
• Evaluating reduction quality across multiple music genres

Innovation

• Graph-based melody reduction via shortest path
• Automatic across pop, folk, classical genres
• Generates symbolic music variations effectively