Conference paper Open Access

A Convolutional Approach to Melody Line Identification in Symbolic Scores

Simonetta, Federico; Cancino-Chacón, Carlos; Ntalampiras, Stavros; Widmer, Gerhard


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3530592</identifier>
  <creators>
    <creator>
      <creatorName>Simonetta, Federico</creatorName>
      <givenName>Federico</givenName>
      <familyName>Simonetta</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5928-9836</nameIdentifier>
      <affiliation>University of Milano</affiliation>
    </creator>
    <creator>
      <creatorName>Cancino-Chacón, Carlos</creatorName>
      <givenName>Carlos</givenName>
      <familyName>Cancino-Chacón</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5770-7005</nameIdentifier>
      <affiliation>Johannes Kepler University</affiliation>
    </creator>
    <creator>
      <creatorName>Ntalampiras, Stavros</creatorName>
      <givenName>Stavros</givenName>
      <familyName>Ntalampiras</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3482-9215</nameIdentifier>
      <affiliation>University of Milano</affiliation>
    </creator>
    <creator>
      <creatorName>Widmer, Gerhard</creatorName>
      <givenName>Gerhard</givenName>
      <familyName>Widmer</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3531-1282</nameIdentifier>
      <affiliation>Johannes Kepler University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Convolutional Approach to Melody Line Identification in Symbolic Scores</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Music Information Retrieval</subject>
    <subject>Convolutional Neural Network</subject>
    <subject>Melody Identification</subject>
    <subject>Symbolic scores</subject>
    <subject>MIR</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-11-04</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3530592</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo" resourceTypeGeneral="Text">10.5281/zenodo.3527965</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo" resourceTypeGeneral="Text">https://arxiv.org/abs/1906.10547</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3530591</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/federicosimonetta</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
</resource>
42
27
views
downloads
All versions This version
Views 4242
Downloads 2727
Data volume 40.9 MB40.9 MB
Unique views 3333
Unique downloads 2222

Share

Cite as