Sonalyzer-Moz: A Framework for Analyzing the Structure of Mozart's Sonata Form

2026-05-18

AI Summary

Automatically analyzing the high-level structure of Mozart’s sonata form is highly challenging, primarily due to the scarcity of annotated data and the specialized music-theoretic expertise required. This work addresses this gap by introducing SoSA-Moz, the first large-scale hierarchically annotated dataset for sonata-form analysis, and proposes Sonalyzer-Moz, a novel framework that integrates feature aggregation with sequence modeling to automatically identify boundaries of high-level sonata sections—namely exposition, development, and recapitulation. By establishing a robust baseline model and demonstrating the efficacy of music information retrieval techniques in parsing complex formal structures, this study not only fills a critical data void in systematic research on sonata form but also advances the feasibility of automated structural analysis in classical music.

Abstract

The sonata form is a musically rich and hierarchically structured form that poses significant challenges for automatic analysis. While music structure analysis has seen strides of progress in recent years, sonata form analysis remains in its early stages. This is largely due to the time-consuming and high barrier of the music background requirement for annotating classical music structures. To advance research in this area, we curated SoSA-Moz, the first large-scale dataset featuring comprehensive hierarchical structure annotations. This work establishes a foundation for systematic sonata form analysis. Leveraging this newly contributed resource, we further propose Sonalyzer-Moz, a baseline model specifically designed for investigating complex sonata structures. This framework integrates feature aggregation with sequential modeling, enabling it to capture both local feature and upper-level structural dependencies. Experiment results show that Sonalyzer-Moz is capable of identifying the components' boundaries of the upper-level structure that are critical to understanding sonata form. Therefore, this method demonstrates, for the first time, the effectiveness of automatic upper-level analysis of sonata form, and provides a robust baseline for future research in the automatic understanding of sonata form while advancing the study of classical music structure analysis.

Problem

• sonata form
• music structure analysis
• hierarchical structure
• automatic analysis
• classical music

Innovation

• sonata form analysis
• hierarchical structure annotation
• automatic music analysis
• feature aggregation
• sequential modeling