Dataset Open Access

Synth-Salience Choral Set

Helena Cuesta; Emilia Gómez


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  <identifier identifierType="DOI">10.5281/zenodo.6534429</identifier>
  <creators>
    <creator>
      <creatorName>Helena Cuesta</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8531-4487</nameIdentifier>
      <affiliation>Universitat Pompeu Fabra</affiliation>
    </creator>
    <creator>
      <creatorName>Emilia Gómez</creatorName>
      <affiliation>Joint Research Centre</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Synth-Salience Choral Set</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-05-10</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6534429</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6534428</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0.0</version>
  <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;The &lt;strong&gt;Synth-salience Choral Set&lt;/strong&gt; (SSCS) is a publicly available dataset for voice assignment based on pitch salience.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The dataset was created to support research on voice assignment based on pitch salience.&amp;nbsp;By definition, an &amp;ldquo;ideal&amp;rdquo; pitch salience representation of a music recording is zero everywhere where there is no perceptible pitch, and has a positive value that reflects the pitches&amp;rsquo; perceived energy at the frequency bins of the corresponding F0 values. In practice, for a normalized synthetic pitch salience function we assume a value equal to the maximum energy (salience), i. e., 1, in the time-frequency bins that correspond to the notes present in a song, and 0 elsewhere. We obtain such a synthetic pitch salience representation directly by processing the digital (MusicXML, MIDI) score of a music piece, using the desired time and frequency quantization, i. e., a time-frequency grid.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;To build the SSCS, we collect scores of four-part (SATB) a cappella choral music from the &lt;a href="https://www.cpdl.org/wiki/index.php/Main_Page"&gt;Choral Public Domain Library (CPDL)&lt;/a&gt;&amp;nbsp;using their API. We assemble a collection of &lt;strong&gt;5381 scores&lt;/strong&gt; in MusicXML format, which we subsequently convert into MIDI files for an easier parsing.&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
Each song in the dataset comprises five CSV files: one with the polyphonic pitch salience representation of the four voices (*_mix.csv) and four additional files with the monophonic pitch salience representation of each voice separately (*_S/A/T/B.csv). In both cases, the asterisk refers to the name of the song, which is shared between all representations from the same song.&lt;br&gt;
Besides the pitch salience files, we provide a metadata CSV file (sscs_metadata.csv) which indicates the associated CPDL URL for each song in the dataset.&amp;nbsp;Note that this dataset contains the input/output features used in the cited&amp;nbsp;study, i.e., salience functions, and not audio files nor scores. However, the accompanying&amp;nbsp;metadata file allows researchers to access the associated open access scores for each example in the dataset.&lt;/p&gt;

&lt;p&gt;When using this dataset for your research, please cite:&lt;/p&gt;

&lt;p&gt;Helena Cuesta and Emilia G&amp;oacute;mez (2022).&amp;nbsp;&lt;strong&gt;Voice Assignment in Vocal Quartets using Deep Learning Models based on Pitch Salience&lt;/strong&gt;. Transactions of the International Society for Music Information Retrieval (TISMIR).&amp;nbsp;&lt;em&gt;To appear.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Helena Cuesta (2022). &lt;strong&gt;Data-driven Pitch Content Description of Choral Singing Recordings&lt;/strong&gt;. PhD thesis. Universitat Pompeu Fabra, Barcelona.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
</resource>
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