This repository contains the code, the results and the datasets used for the work presented at the ISMIR 2019 about an approach for using Convolutional Neural Networks for melody identification in symbolic scores. You can surf this website, view and clone it on github or download the entire archive
A work by OFAI - Austrian Research Institute for Artificial Intelligence & LIM - Music Informatics Laboratory
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.
Full paper: link
The method starts from a score and tries to identify the notes belonging to the melody.
Starting from here
it attempts to output this
To achieve that, we use a Convolutional Neural Network and a graph approach. First, the score is converted into a Boolean pianoroll; then, the pianoroll is processed by the CNN whose output is a new pianoroll where each pixel has a probability value of being a part of the melody. Based on this probabilities, we can reconstruct the melody line.
We show that our method is better than sate-of-art, but that further effort is needed.
Please, cite us as:
Federico Simonetta, Carlos Cancino-Chacón, Stavros Ntalampiras & Gerhard Widmer. (2019). "A Convolutional Approach to Melody Line Identification in Symbolic Scores". In Proceedings of the 20th International Society for Music Information Retrieval Conference. Delft, The Netherlands.
This research has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 670035 (project "Con Espressione").
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.
We thank Elaine Chew for sharing the code for VoSA. We thank Laura Bishop for proofreading an earlier version of this manuscript.