# A Convolutional Approach to Melody Line Identification in Symbolic Scores

arXiv: https://arxiv.org/abs/1906.10547 · HTML: https://mlsanity.com/paper/1906.10547/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [BERT-like Pre-training for Symbolic Piano Music Classification Tasks](https://mlsanity.com/paper/2107.05223/) (2021)
- [PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2407.03361/) (2024)
- [Optical Music Recognition in Manuscripts from the Ricordi Archive](https://mlsanity.com/paper/2408.10260/) (2024)
- [Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey](https://mlsanity.com/paper/2506.12440/) (2025)
- [Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction](https://mlsanity.com/paper/2507.04776/) (2025)
- [Let the Model Learn to Feel: Mode-Guided Tonality Injection for Symbolic Music Emotion Recognition](https://mlsanity.com/paper/2512.17946/) (2025)

## Abstract (arXiv)

In many musical traditions, the melody line is of primary significance in a piece. Human listeners can readily distinguish melodies from accompaniment; however, making this distinction given only the written score -- i.e. without listening to the music performed -- can be a difficult task. Solving this task is of great importance for both Music Information Retrieval and musicological applications. In this paper, we propose an automated approach to identifying the most salient melody line in a symbolic score. The backbone of the method consists of a convolutional neural network (CNN) estimating the probability that each note in the score (more precisely: each pixel in a piano roll encoding of the score) belongs to the melody line. We train and evaluate the method on various datasets, using manual annotations where available and solo instrument parts where not. We also propose a method to inspect the CNN and to analyze the influence exerted by notes on the prediction of other notes; this method can be applied whenever the output of a neural network has the same size as the input.