MusicSynth: An Automated Pipeline for Generating Violin Fingerboard Animations from Sheet Music Using Optical Music Recognition

Abhimanyu Kaushik

arxiv: 2605.17181 · v1 · pith:LKUCTR6Inew · submitted 2026-05-16 · 💻 cs.SD · cs.AI

MusicSynth: An Automated Pipeline for Generating Violin Fingerboard Animations from Sheet Music Using Optical Music Recognition

Abhimanyu Kaushik This is my paper

Pith reviewed 2026-05-20 13:55 UTC · model grok-4.3

classification 💻 cs.SD cs.AI

keywords violinoptical music recognitionfingerboard animationsheet musicmusic educationMusicXMLweb tool

0 comments

The pith

MusicSynth turns violin sheet music photos into automatic fingerboard animation videos.

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

MusicSynth is a web tool that lets a user upload a photo of violin sheet music or a digital score file and receive a video showing the fingerboard with the correct finger position highlighted for each note at the right time. The pipeline joins an existing optical music recognition library to extract notes from the image, a MusicXML parser to get timing, a custom lookup table to translate each note into a string and finger, and a renderer that builds the video frame by frame. Tests on 110 public-domain violin scores found 91.2 percent correct note identification from clean printed images and 99.1 percent correct finger assignments from digital files. The work shows that no manual note entry or software installation is required to produce these tutorials.

Core claim

The paper shows that linking optical music recognition, MusicXML timing extraction, and a purpose-built note-to-finger lookup table produces a complete, browser-only pipeline that converts violin sheet music into timed fingerboard animations.

What carries the argument

A custom lookup table that maps each musical note to the corresponding violin string and finger position on the fingerboard.

If this is right

Beginners can follow sheet music directly on a visual fingerboard without memorizing positions in advance.
The process requires only a browser and an image or file upload, with no manual transcription or extra software.
Accuracy above 90 percent on public-domain scores indicates the approach works for typical clean printed violin repertoire.
The open-source design makes the mapping table and pipeline available for reuse or modification.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same lookup-table approach could be adapted for viola or cello by changing only the string lengths and position data.
Replacing the optical music recognition step with a more robust model might extend the tool to handwritten scores or dense orchestral parts.
The generated videos could feed into practice apps that overlay real-time finger guidance during playback.

Load-bearing premise

The chosen optical music recognition library accurately detects notes in images of standard printed violin music and the lookup table correctly assigns finger positions for the tested pieces without adjustments for different editions or fingerings.

What would settle it

A new test set of clean printed violin scores on which the system detects fewer than 80 percent of notes correctly or assigns the wrong finger more than 5 percent of the time.

Figures

Figures reproduced from arXiv: 2605.17181 by Abhimanyu Kaushik.

**Figure 2.** Figure 2: Input: a smartphone photograph of the first page of [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Three frames from the MusicSynth output video for [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

read the original abstract

Learning the violin is harder than it looks. Unlike piano keys or guitar frets, the violin neck has no markings at all, so a beginner cannot tell by looking where to place each finger. MusicSynth is an open-source web tool that tries to fix that: user uploads a photo of any violin sheet music (or a digital score file), and the system automatically produces a video showing a violin fingerboard with each note highlighted at the right moment -- no software to install, no manual note entry required. The system connects three existing open-source tools into one pipeline: an optical music recognition (OMR) library reads the notes from the uploaded image, a MusicXML parser extracts timing information from digital scores, and a video renderer draws the fingerboard frame by frame. The only part built from scratch is the lookup table that maps each musical note to a string and finger position on the violin. Tested across 110 public-domain violin scores, MusicSynth correctly identified 91.2\,\% of notes in clean printed music and assigned the right finger position 99.1\,\% of the time when given a digital score file. To the author's knowledge, no freely available tool currently turns a sheet music image into an animated violin fingerboard tutorial automatically and in a single browser-based step.

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.

Desk Editor's Note private letter to a colleague

MusicSynth wires existing OMR tools to a new violin finger lookup table for a browser demo, but reports no end-to-end accuracy on the image-to-animation path that matters most.

read the letter

This paper describes a simple open-source web tool that uploads a violin score image, runs it through an OMR library to pull out notes, then uses a custom lookup table to map each note to a string and finger position before rendering a timed animation of the fingerboard. The only original component is that lookup table; everything else comes from existing libraries for OMR and MusicXML parsing. The system is fully browser-based and requires no local install, which is a clear practical plus for beginners or teachers who want quick visuals. They tested on 110 public-domain scores and give two numbers: 91.2 percent note detection from clean printed images and 99.1 percent correct finger assignments when fed digital score files directly. That second figure shows the lookup table itself works reliably on accurate input. The soft spot is exactly what the stress test flagged. The main claimed workflow starts with an image, yet the paper never measures how often the full pipeline produces the right finger positions once OMR errors are included. Note mistakes at 8-9 percent will directly create wrong finger highlights, but no combined figure is supplied. Test-set construction is also thin; there is no breakdown by score difficulty, rhythm density, or image quality, so it is hard to judge how well it holds up on real student scans. This is an applied engineering note rather than a research advance in OMR or music representation. It will interest violin teachers or developers building educational prototypes who need a working starting point. It does not reshape any broader field. The evaluation is basic but reproducible enough on public scores, and the code is open, so the work is honest on its own terms. I would send it to peer review. A referee can request the missing end-to-end metric and a clearer error analysis without much trouble, and the tool itself is complete enough to be worth documenting.

Referee Report

1 major / 2 minor

Summary. The manuscript presents MusicSynth, a web-based open-source tool that generates animated violin fingerboard tutorials from uploaded sheet music images or digital score files. The pipeline combines an optical music recognition (OMR) library to extract notes from images, a MusicXML parser for timing information, and a custom lookup table to map notes to violin string and finger positions. The system renders a video showing the fingerboard with highlighted positions synchronized to the music. Testing on 110 public-domain violin scores yielded 91.2% accuracy in note identification for clean printed music and 99.1% accuracy in finger position assignment for digital score files.

Significance. If the end-to-end performance is validated, this could provide a practical educational resource for violin learners by automating finger placement visualization in an accessible browser interface. The work's strengths lie in its open-source release, integration of existing OMR and parsing libraries without new algorithmic invention, and focus on a complete user workflow from image upload to animation output.

major comments (1)

[Abstract] The abstract reports 91.2% note identification accuracy on clean printed music images and 99.1% finger position accuracy on digital score files, but provides no combined end-to-end finger position accuracy for the image-to-animation workflow that constitutes the primary use case. Since the custom lookup table is applied directly to OMR output, note detection errors propagate to finger assignments, and this missing metric is load-bearing for the central claim of automatic tutorial generation from photos.

minor comments (2)

[Evaluation] The construction of the 110-score test set, including criteria for selection, handling of edge cases such as complex rhythms or varying image quality, and breakdown of error types, is not described. This limits assessment of the robustness of the reported figures.
The lookup table is described as mapping notes to string and finger positions, but it is unclear how it handles alternative fingerings, position changes, or variations across different editions of the same score.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for pointing out the absence of an end-to-end accuracy metric for the image-to-animation pipeline. We agree this is important and have planned revisions to address it, as detailed in our response below.

read point-by-point responses

Referee: [Abstract] The abstract reports 91.2% note identification accuracy on clean printed music images and 99.1% finger position accuracy on digital score files, but provides no combined end-to-end finger position accuracy for the image-to-animation workflow that constitutes the primary use case. Since the custom lookup table is applied directly to OMR output, note detection errors propagate to finger assignments, and this missing metric is load-bearing for the central claim of automatic tutorial generation from photos.

Authors: We fully acknowledge the referee's concern. The reported 91.2% reflects OMR performance on images, and 99.1% reflects the accuracy of our note-to-finger lookup table when provided with correct note inputs from digital files. Errors in note detection will indeed lead to incorrect finger assignments in the end-to-end system. To strengthen the manuscript, we will add a combined metric in the revised version: we will evaluate finger position accuracy by feeding OMR outputs from the image tests into the lookup table and comparing against ground-truth finger positions. This new result will be included in the abstract and the experimental evaluation section. revision: yes

Circularity Check

0 steps flagged

No circularity: engineering pipeline evaluated on independent public-domain scores

full rationale

The paper presents an implementation pipeline that wires together three existing open-source components (OMR library, MusicXML parser, video renderer) plus one custom lookup table for note-to-finger mapping. All reported performance figures (91.2 % note identification on printed images, 99.1 % finger-position accuracy on digital scores) are direct empirical measurements on 110 external public-domain violin scores. No equations, derivations, fitted parameters, or first-principles claims appear; the work contains no self-referential definitions, no predictions that reduce to inputs by construction, and no load-bearing self-citations. The central contribution is therefore an engineering artifact whose correctness is assessed against independent data, satisfying the criteria for a self-contained, non-circular result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that off-the-shelf OMR performs adequately on violin scores and that a static lookup table suffices for finger mapping; no free parameters are fitted to data in the reported results.

axioms (1)

domain assumption Existing optical music recognition libraries can reliably extract note information from images of clean printed violin sheet music.
The pipeline depends on an OMR library to read notes from uploaded photos without additional training or violin-specific adaptations mentioned.

invented entities (1)

Note-to-string-and-finger lookup table no independent evidence
purpose: Converts each detected musical note into the corresponding violin string and finger position for animation.
This table is the only component built from scratch and is required for the fingerboard visualization.

pith-pipeline@v0.9.0 · 5764 in / 1385 out tokens · 79426 ms · 2026-05-20T13:55:03.653086+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

The only part built from scratch is the lookup table that maps each musical note to a string and finger position on the violin.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages

[1]

oemer: End-to-end Optical Music Recognition.https://github

BreezeWhite (2022). oemer: End-to-end Optical Music Recognition.https://github. com/BreezeWhite/oemer

work page 2022
[2]

Calvo-Zaragoza, J., Ha, J., & Rios-Vila, A. (2021). Understanding optical music recog- nition.ACM Computing Surveys, 53(4), 1–35

work page 2021
[3]

De Prisco, R., Malandrino, D., Zaccagnino, G., & Zaccagnino, R. (2020). Multimodal music learning: Combining audio, visual, and interactive feedback.Multimedia Tools and Applications, 79, 20745–20769

work page 2020
[4]

Duke, R. A. (2005).Intelligent Music Teaching. Learning and Behavior Resources

work page 2005
[5]

(1962).Principles of Violin Playing and Teaching

Galamian, I. (1962).Principles of Violin Playing and Teaching. Prentice-Hall

work page 1962
[6]

Good, M. (2001). MusicXML: An internet-friendly format for sheet music. InProc. XML 2001 Conference(pp. 12–14)

work page 2001
[7]

A., Durand, O., & Engel, D

Huang, C.-Z. A., Durand, O., & Engel, D. (2014). Synthesia: Game-based piano learn- ing. InProc. NIME(pp. 1–4)

work page 2014
[8]

MuseScore 4: Open-source music notation software.https: //musescore.org

MuseScore Team (2023). MuseScore 4: Open-source music notation software.https: //musescore.org. 11

work page 2023
[9]

Performanceerrordetectionandpost- processing for fast and accurate symbolic music alignment

Nakamura, E., Yoshii, K., &Sagayama, S.(2015). Performanceerrordetectionandpost- processing for fast and accurate symbolic music alignment. InProc. ISMIR(pp. 347– 353)

work page 2015
[10]

Radisavljevic, A., & Driessen, P. (2004). Path difference learning for guitar fingering problem. InProc. ICMC(pp. 1–4)

work page 2004
[11]

Ramirez, R., Vamvakousis, A., & Maestre, S. (2018). An augmented-reality violin tutorial system. InProc. ISMIR(pp. 423–430)

work page 2018
[12]

Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A. R. S., Guedes, C., & Cardoso, J. S. (2012). Optical music recognition: State-of-the-art and open issues.Int. J. Multimedia Information Retrieval, 1(3), 173–190

work page 2012
[13]

(1996).The Computer Music Tutorial

Roads, C. (1996).The Computer Music Tutorial. MIT Press

work page 1996
[14]

Streamlit Inc. (2019). Streamlit: The fastest way to build data apps.https: //streamlit.io

work page 2019
[15]

(1978).Suzuki Violin School, Volumes 1–3

Suzuki, S. (1978).Suzuki Violin School, Volumes 1–3. Summy-Birchard

work page 1978
[16]

Tuggener, L., et al. (2018). Deep watershed detector for music object recognition. In Proc. ISMIR(pp. 1–8). 12

work page 2018

[1] [1]

oemer: End-to-end Optical Music Recognition.https://github

BreezeWhite (2022). oemer: End-to-end Optical Music Recognition.https://github. com/BreezeWhite/oemer

work page 2022

[2] [2]

Calvo-Zaragoza, J., Ha, J., & Rios-Vila, A. (2021). Understanding optical music recog- nition.ACM Computing Surveys, 53(4), 1–35

work page 2021

[3] [3]

De Prisco, R., Malandrino, D., Zaccagnino, G., & Zaccagnino, R. (2020). Multimodal music learning: Combining audio, visual, and interactive feedback.Multimedia Tools and Applications, 79, 20745–20769

work page 2020

[4] [4]

Duke, R. A. (2005).Intelligent Music Teaching. Learning and Behavior Resources

work page 2005

[5] [5]

(1962).Principles of Violin Playing and Teaching

Galamian, I. (1962).Principles of Violin Playing and Teaching. Prentice-Hall

work page 1962

[6] [6]

Good, M. (2001). MusicXML: An internet-friendly format for sheet music. InProc. XML 2001 Conference(pp. 12–14)

work page 2001

[7] [7]

A., Durand, O., & Engel, D

Huang, C.-Z. A., Durand, O., & Engel, D. (2014). Synthesia: Game-based piano learn- ing. InProc. NIME(pp. 1–4)

work page 2014

[8] [8]

MuseScore 4: Open-source music notation software.https: //musescore.org

MuseScore Team (2023). MuseScore 4: Open-source music notation software.https: //musescore.org. 11

work page 2023

[9] [9]

Performanceerrordetectionandpost- processing for fast and accurate symbolic music alignment

Nakamura, E., Yoshii, K., &Sagayama, S.(2015). Performanceerrordetectionandpost- processing for fast and accurate symbolic music alignment. InProc. ISMIR(pp. 347– 353)

work page 2015

[10] [10]

Radisavljevic, A., & Driessen, P. (2004). Path difference learning for guitar fingering problem. InProc. ICMC(pp. 1–4)

work page 2004

[11] [11]

Ramirez, R., Vamvakousis, A., & Maestre, S. (2018). An augmented-reality violin tutorial system. InProc. ISMIR(pp. 423–430)

work page 2018

[12] [12]

Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A. R. S., Guedes, C., & Cardoso, J. S. (2012). Optical music recognition: State-of-the-art and open issues.Int. J. Multimedia Information Retrieval, 1(3), 173–190

work page 2012

[13] [13]

(1996).The Computer Music Tutorial

Roads, C. (1996).The Computer Music Tutorial. MIT Press

work page 1996

[14] [14]

Streamlit Inc. (2019). Streamlit: The fastest way to build data apps.https: //streamlit.io

work page 2019

[15] [15]

(1978).Suzuki Violin School, Volumes 1–3

Suzuki, S. (1978).Suzuki Violin School, Volumes 1–3. Summy-Birchard

work page 1978

[16] [16]

Tuggener, L., et al. (2018). Deep watershed detector for music object recognition. In Proc. ISMIR(pp. 1–8). 12

work page 2018