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arxiv: 2605.18175 · v1 · pith:RVAOZZ4Xnew · submitted 2026-05-18 · 💻 cs.SD

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

Jing Zhao , KokSheik Wong , Vishnu Monn Baskaran , Kiki Adhinugraha , David Taniar This is my paper

Pith reviewed 2026-05-20 00:11 UTC · model grok-4.3

classification 💻 cs.SD
keywords sonata formMozartmusic structure analysishierarchical annotationautomatic analysisSoSA-Moz datasetSonalyzer-Moz
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The pith

A baseline model using a new annotated dataset can automatically identify the upper-level structural boundaries in Mozart sonatas.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper curates SoSA-Moz, the first large-scale dataset with hierarchical annotations of sonata form in Mozart's works, addressing the high barrier of expert music knowledge needed for such labeling. It then introduces Sonalyzer-Moz, which combines feature aggregation and sequential modeling to detect both local details and higher-level dependencies in the form. Experiments demonstrate that this approach locates key component boundaries at the upper level of sonata structure. A sympathetic reader would care because sonata form analysis has resisted automation, and progress here could support broader automatic understanding of classical music hierarchies.

Core claim

By contributing the SoSA-Moz dataset of comprehensively annotated Mozart sonatas and proposing the Sonalyzer-Moz framework that integrates feature aggregation with sequential modeling, the work shows for the first time that automatic identification of upper-level sonata form component boundaries is feasible, establishing a baseline for future research in systematic classical music structure analysis.

What carries the argument

Sonalyzer-Moz, a framework that integrates feature aggregation with sequential modeling to capture local features alongside upper-level structural dependencies.

Load-bearing premise

The human annotations in the SoSA-Moz dataset correctly and consistently mark the hierarchical boundaries of sonata form as music theory experts define them.

What would settle it

A new set of independent expert annotations on the same Mozart pieces that systematically disagrees with the SoSA-Moz labels on upper-level boundaries, or test results showing the model fails to locate those boundaries on additional unseen sonatas.

Figures

Figures reproduced from arXiv: 2605.18175 by David Taniar, Jing Zhao, Kiki Adhinugraha, KokSheik Wong, Vishnu Monn Baskaran.

Figure 1
Figure 1. Figure 1: Structural visualization of the sonata form across two performance versions of Mozart’s Sonata K.311, first movement. only certain movements are in sonata form, for example, the first or last movements [7]. Additionally, many symphonies, particularly those from the Classical and Romantic periods, are composed in sonata form. Our work here focuses exclusively on movements that are in the sonata form. A visu… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of the Sonalyzer-Moz framework. TABLE II RESULTS FOR DIFFERENT HYPERPARAMETER VALUES. HERE, ONLY THE TOP 2 RESULTS FOR EACH γ SETTING ARE RECORDED. (C, hlstm, Llstm) HR3P (%) HR3R (%) HR3F (%) (10, 1024, 5) 76.47 77.17 76.24 (15, 256, 5) 68.46 78.44 72.38 (10, 512, 5) 67.04 79.22 71.97 (15, 512, 3) 62.92 76.50 68.47 (10, 1024, 3) 59.99 77.22 66.33 set γ to correspond to the number of frames i… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces SoSA-Moz, the first large-scale dataset with comprehensive hierarchical structure annotations for Mozart's sonata forms, and proposes Sonalyzer-Moz, a baseline model that integrates feature aggregation with sequential modeling to capture local features and upper-level structural dependencies. Experiments are reported to show that the model identifies critical upper-level boundaries (exposition, development, recapitulation, etc.), establishing for the first time the effectiveness of automatic upper-level sonata form analysis and providing a baseline for future work.

Significance. If the central claims hold after addressing evaluation and annotation details, the work would be significant as the first large curated resource and baseline specifically targeting the hierarchical upper-level structure of sonata form, a task that has lagged in music information retrieval due to annotation difficulty. The contribution of an external dataset rather than self-referential fitting reduces circularity risk and could enable reproducible progress in computational musicology.

major comments (2)
  1. [Abstract] Abstract: The claim that 'Experiment results show that Sonalyzer-Moz is capable of identifying the components' boundaries of the upper-level structure' is load-bearing for the 'first-time effectiveness' assertion, yet the abstract provides no evaluation metrics, baselines, dataset splits, error bars, or statistical tests. Without these, it is impossible to determine whether the reported boundary detection reflects genuine musical structure or dataset-specific artifacts.
  2. [Abstract] Abstract: The central claim depends on the SoSA-Moz hierarchical annotations correctly capturing expert-defined sonata form boundaries. However, despite explicitly noting the 'high barrier of required music background knowledge' for annotation, the manuscript reports no inter-annotator agreement scores, adjudication protocol, or validation against independent experts. This is a load-bearing assumption that must be addressed to substantiate that model success demonstrates automatic analysis effectiveness rather than annotation consistency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'Experiment results show that Sonalyzer-Moz is capable of identifying the components' boundaries of the upper-level structure' is load-bearing for the 'first-time effectiveness' assertion, yet the abstract provides no evaluation metrics, baselines, dataset splits, error bars, or statistical tests. Without these, it is impossible to determine whether the reported boundary detection reflects genuine musical structure or dataset-specific artifacts.

    Authors: We agree that the abstract should be more self-contained to support the central claims. In the revised version, we will expand the abstract to report key quantitative results, including boundary detection F1 scores for upper-level sections, the specific baselines compared (e.g., HMM and simple threshold methods), the train/validation/test split ratios used, and a brief note on statistical testing. These details are present in the experimental section of the full manuscript; the revision will ensure the abstract stands alone without altering the reported findings. revision: yes

  2. Referee: [Abstract] Abstract: The central claim depends on the SoSA-Moz hierarchical annotations correctly capturing expert-defined sonata form boundaries. However, despite explicitly noting the 'high barrier of required music background knowledge' for annotation, the manuscript reports no inter-annotator agreement scores, adjudication protocol, or validation against independent experts. This is a load-bearing assumption that must be addressed to substantiate that model success demonstrates automatic analysis effectiveness rather than annotation consistency.

    Authors: We acknowledge the importance of documenting annotation reliability. The SoSA-Moz annotations were created by a single expert musicologist with extensive experience in Mozart analysis, following standard musicological references for sonata form boundaries. While inter-annotator agreement was not computed due to the specialized expertise required and resource constraints, we will add a dedicated subsection describing the annotation protocol, including the use of published analyses for cross-validation on a subset of works and the resolution of ambiguous cases through reference to authoritative sources. This revision will clarify the annotation process without overstating its scope. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper contributes a new dataset SoSA-Moz with hierarchical sonata form annotations and evaluates a baseline model Sonalyzer-Moz on it using feature aggregation and sequential modeling. No equations, fitted parameters, or self-citations are presented that reduce the effectiveness claim to inputs by construction. The work is self-contained through new data contribution and standard experimental validation on that data, with no load-bearing self-referential steps or renamings of known results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on abstract only: the central claims rest on the assumption that expert annotations exist and are reliable, plus standard machine-learning assumptions about feature quality and sequential modeling sufficiency. No explicit free parameters, axioms, or invented entities are described.

axioms (1)
  • domain assumption Human-provided hierarchical annotations in SoSA-Moz accurately reflect music-theoretic sonata form boundaries
    Abstract notes the high barrier of music background required for annotation, making this the load-bearing premise for all downstream claims.

pith-pipeline@v0.9.0 · 5760 in / 1429 out tokens · 55708 ms · 2026-05-20T00:11:21.128079+00:00 · methodology

discussion (0)

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Reference graph

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