Conference paper Open Access

Multimodal Music Information Processing and Retrieval: Survey and Future Challenges

Simonetta, Federico; Ntalampiras, Stavros; Avanzini, Federico


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  <identifier identifierType="URL">https://zenodo.org/record/2565059</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 Milan</affiliation>
    </creator>
    <creator>
      <creatorName>Ntalampiras, Stavros</creatorName>
      <givenName>Stavros</givenName>
      <familyName>Ntalampiras</familyName>
      <affiliation>University of Milan</affiliation>
    </creator>
    <creator>
      <creatorName>Avanzini, Federico</creatorName>
      <givenName>Federico</givenName>
      <familyName>Avanzini</familyName>
      <affiliation>University of Milan</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Multimodal Music Information Processing and Retrieval: Survey and Future Challenges</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Multimodal music processing</subject>
    <subject>music information retrieval</subject>
    <subject>music description systems</subject>
    <subject>information fusion</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-01-24</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2565059</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="ISBN" relationType="IsPartOf">978-1-72811-649-5</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="arXiv" relationType="IsIdenticalTo">arXiv:1902.05347</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/MMRP.2019.8665366</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/federicosimonetta</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/mir</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://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;Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, midlevel representations, motion and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval, and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years.&lt;/p&gt;</description>
  </descriptions>
</resource>
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