Abstract
This paper presents an unsupervised machine learning algorithm that identifies recurring patterns -- referred to as ``music-words'' -- from symbolic music data. These patterns are fundamental to musical structure and reflect the cognitive processes involved in composition. However, extracting these patterns remains challenging because of the inherent semantic ambiguity in musical interpretation. We formulate the task of music-word discovery as a statistical optimization problem and propose a two-stage Expectation-Maximization (EM)-based learning framework: 1. Developing a music-word dictionary; 2. Reconstructing the music data. When evaluated against human expert annotations, the algorithm achieved an Intersection over Union (IoU) score of 0.61. Our findings indicate that minimizing code length effectively addresses semantic ambiguity, suggesting that human optimization of encoding systems shapes musical semantics. This approach enables computers to extract ``basic building blocks'' from music data, facilitating structural analysis and sparse encoding. The method has two primary applications. First, in AI music, it supports downstream tasks such as music generation, classification, style transfer, and improvisation. Second, in musicology, it provides a tool for analyzing compositional patterns and offers insights into the principle of minimal encoding across diverse musical styles and composers.
Abstract (translated)
本文介绍了一种无监督机器学习算法，该算法能够从符号音乐数据中识别出重复出现的模式——称为“音乐词汇”（music-words）。这些模式是音乐结构的基本组成部分，并反映了作曲过程中的认知过程。然而，由于音乐解释中存在的语义模糊性，提取这些模式仍然是一个挑战。我们把发现音乐词汇的任务形式化为一个统计优化问题，并提出了一种基于两阶段期望最大化（EM）的学习框架：1. 创建音乐词典；2. 重构音乐数据。 在与人类专家标注进行对比评估时，该算法达到了0.61的交并比（IoU）得分。我们的研究结果表明，通过最小化编码长度可以有效地解决语义模糊性问题，暗示了人类优化编码系统对音乐语义的影响。这种方法使计算机能够从音乐数据中提取“基本构建模块”，从而促进结构分析和稀疏编码。 该方法有两个主要应用领域：首先，在AI音乐方面，它支持下游任务如音乐生成、分类、风格转换及即兴创作；其次，在音乐学研究中，它提供了一种用于分析作曲模式的工具，并为不同音乐流派及作曲家中的最小化编码原则提供了见解。
URL
https://arxiv.org/abs/2509.24603
