# Barwise Section Boundary Detection in Symbolic Music Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2509.16566 · HTML: https://mlsanity.com/paper/2509.16566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio](https://mlsanity.com/paper/2307.16425/) (2023)
- [Automatic Analysis and Influence of Hierarchical Structure on Melody, Rhythm and Harmony in Popular Music](https://mlsanity.com/paper/2010.07518/) (2020)
- [Unsupervised Learning of Deep Features for Music Segmentation](https://mlsanity.com/paper/2108.12955/) (2021)
- [To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions](https://mlsanity.com/paper/2205.14700/) (2022)
- [What is missing in deep music generation? A study of repetition and structure in popular music](https://mlsanity.com/paper/2209.00182/) (2022)
- [Composer's Assistant: An Interactive Transformer for Multi-Track MIDI Infilling](https://mlsanity.com/paper/2301.12525/) (2023)
- [SampleMatch: Drum Sample Retrieval by Musical Context](https://mlsanity.com/paper/2208.01141/) (2022)
- [SymPAC: Scalable Symbolic Music Generation With Prompts And Constraints](https://mlsanity.com/paper/2409.03055/) (2024)
- [Unsupervised Symbolic Music Segmentation using Ensemble Temporal Prediction Errors](https://mlsanity.com/paper/2207.00760/) (2022)
- [Cue Point Estimation using Object Detection](https://mlsanity.com/paper/2407.06823/) (2024)
- [Barwise Music Structure Analysis with the Correlation Block-Matching Segmentation Algorithm](https://mlsanity.com/paper/2311.18604/) (2023)

## Abstract (arXiv)

Current methods for Music Structure Analysis (MSA) focus primarily on audio data. While symbolic music can be synthesized into audio and analyzed using existing MSA techniques, such an approach does not exploit symbolic music's rich explicit representation of pitch, timing, and instrumentation. A key subproblem of MSA is section boundary detection-determining whether a given point in time marks the transition between musical sections. In this paper, we study automatic section boundary detection for symbolic music. First, we introduce a human-annotated MIDI dataset for section boundary detection, consisting of metadata from 6134 MIDI files that we manually curated from the Lakh MIDI dataset. Second, we train a deep learning model to classify the presence of section boundaries within a fixed-length musical window. Our data representation involves a novel encoding scheme based on synthesized overtones to encode arbitrary MIDI instrumentations into 3-channel piano rolls. Our model achieves an F1 score of 0.77, improving over the analogous audio-based supervised learning approach and the unsupervised block-matching segmentation (CBM) audio approach by 0.22 and 0.31, respectively. We release our dataset, code, and models.