Quantifying Auditory Temporal Stability in a Large Database of Recorded Music
Identifieur interne : 000017 ( Pmc/Curation ); précédent : 000016; suivant : 000018Quantifying Auditory Temporal Stability in a Large Database of Recorded Music
Auteurs : Robert J. Ellis ; Zhiyan Duan ; Ye WangSource :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
“Moving to the beat” is both one of the most basic and one of the most profound means by which humans (and a few other species) interact with music. Computer algorithms that detect the precise temporal location of beats (i.e., pulses of musical “energy”) in recorded music have important practical applications, such as the creation of playlists with a particular tempo for rehabilitation (e.g., rhythmic gait training), exercise (e.g., jogging), or entertainment (e.g., continuous dance mixes). Although several such algorithms return simple point estimates of an audio file’s temporal structure (e.g., “average tempo”, “time signature”), none has sought to quantify the temporal
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DOI: 10.1371/journal.pone.0110452
PubMed: 25469636
PubMed Central: 4254286
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Quantifying Auditory Temporal Stability in a Large Database of Recorded Music</title><author><name sortKey="Ellis, Robert J" sort="Ellis, Robert J" uniqKey="Ellis R" first="Robert J." last="Ellis">Robert J. Ellis</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Duan, Zhiyan" sort="Duan, Zhiyan" uniqKey="Duan Z" first="Zhiyan" last="Duan">Zhiyan Duan</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Wang, Ye" sort="Wang, Ye" uniqKey="Wang Y" first="Ye" last="Wang">Ye Wang</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25469636</idno><idno type="pmc">4254286</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254286</idno><idno type="RBID">PMC:4254286</idno><idno type="doi">10.1371/journal.pone.0110452</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">000017</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000017</idno><idno type="wicri:Area/Pmc/Curation">000017</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000017</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Quantifying Auditory Temporal Stability in a Large Database of Recorded Music</title><author><name sortKey="Ellis, Robert J" sort="Ellis, Robert J" uniqKey="Ellis R" first="Robert J." last="Ellis">Robert J. Ellis</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Duan, Zhiyan" sort="Duan, Zhiyan" uniqKey="Duan Z" first="Zhiyan" last="Duan">Zhiyan Duan</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Wang, Ye" sort="Wang, Ye" uniqKey="Wang Y" first="Ye" last="Wang">Ye Wang</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>“Moving to the beat” is both one of the most basic and one of the most profound means by which humans (and a few other species) interact with music. Computer algorithms that detect the precise temporal location of beats (i.e., pulses of musical “energy”) in recorded music have important practical applications, such as the creation of playlists with a particular tempo for rehabilitation (e.g., rhythmic gait training), exercise (e.g., jogging), or entertainment (e.g., continuous dance mixes). Although several such algorithms return simple point estimates of an audio file’s temporal structure (e.g., “average tempo”, “time signature”), none has sought to quantify the temporal <italic>stability</italic> of a series of detected beats. Such a method-a “Balanced Evaluation of Auditory Temporal Stability” (BEATS)–is proposed here, and is illustrated using the Million Song Dataset (a collection of audio features and music metadata for nearly one million audio files). A publically accessible web interface is also presented, which combines the thresholdable statistics of BEATS with queryable metadata terms, fostering potential avenues of research and facilitating the creation of highly personalized music playlists for clinical or recreational applications.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Merker, Bh" uniqKey="Merker B">BH Merker</name></author><author><name sortKey="Madison, Gs" uniqKey="Madison G">GS Madison</name></author><author><name sortKey="Eckerdal, P" uniqKey="Eckerdal P">P Eckerdal</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Winkler, I" uniqKey="Winkler I">I Winkler</name></author><author><name sortKey="Haden, Gp" uniqKey="Haden G">GP Háden</name></author><author><name sortKey="Ladinig, O" uniqKey="Ladinig O">O 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25469636</article-id><article-id pub-id-type="pmc">4254286</article-id><article-id pub-id-type="publisher-id">PONE-D-14-32051</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0110452</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biology and Life Sciences</subject><subj-group><subject>Neuroscience</subject><subj-group><subject>Cognitive Science</subject><subj-group><subject>Cognitive Psychology</subject><subj-group><subject>Music Cognition</subject></subj-group></subj-group></subj-group></subj-group><subj-group><subject>Psychology</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Computer and Information Sciences</subject><subj-group><subject>Data Visualization</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine and Health Sciences</subject><subj-group><subject>Complementary and Alternative Medicine</subject><subj-group><subject>Music Therapy</subject></subj-group></subj-group><subj-group><subject>Neurology</subject><subj-group><subject>Neurodegenerative Diseases</subject><subj-group><subject>Movement Disorders</subject><subj-group><subject>Parkinson Disease</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Research and Analysis Methods</subject><subj-group><subject>Database and Informatics Methods</subject><subj-group><subject>Information Retrieval</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Social Sciences</subject></subj-group></article-categories><title-group><article-title>Quantifying Auditory Temporal Stability in a Large Database of Recorded Music</article-title><alt-title alt-title-type="running-head">Quantifying Auditory Temporal Stability</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Ellis</surname><given-names>Robert J.</given-names></name><xref ref-type="aff" rid="aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Duan</surname><given-names>Zhiyan</given-names></name><xref ref-type="aff" rid="aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>Ye</given-names></name><xref ref-type="aff" rid="aff1"></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>School of Computing, National University of Singapore, Singapore, Singapore</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Robin</surname><given-names>Donald A.</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>University of Texas Health Science Center at San Antonio, Research Imaging Institute, United States of America</addr-line></aff><author-notes><corresp id="cor1">* E-mail: <email>wangye@comp.nus.edu.sg</email></corresp><fn fn-type="conflict"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type="con"><p>Conceived and designed the experiments: RJE YW. Analyzed the data: RJE ZD. Wrote the paper: RJE ZD YW.</p></fn></author-notes><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>3</day><month>12</month><year>2014</year></pub-date><volume>9</volume><issue>12</issue><elocation-id>e110452</elocation-id><history><date date-type="received"><day>17</day><month>7</month><year>2014</year></date><date date-type="accepted"><day>10</day><month>9</month><year>2014</year></date></history><permissions><copyright-year>2014</copyright-year><copyright-holder>Ellis et al</copyright-holder><license><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><abstract><p>“Moving to the beat” is both one of the most basic and one of the most profound means by which humans (and a few other species) interact with music. Computer algorithms that detect the precise temporal location of beats (i.e., pulses of musical “energy”) in recorded music have important practical applications, such as the creation of playlists with a particular tempo for rehabilitation (e.g., rhythmic gait training), exercise (e.g., jogging), or entertainment (e.g., continuous dance mixes). Although several such algorithms return simple point estimates of an audio file’s temporal structure (e.g., “average tempo”, “time signature”), none has sought to quantify the temporal <italic>stability</italic> of a series of detected beats. Such a method-a “Balanced Evaluation of Auditory Temporal Stability” (BEATS)–is proposed here, and is illustrated using the Million Song Dataset (a collection of audio features and music metadata for nearly one million audio files). A publically accessible web interface is also presented, which combines the thresholdable statistics of BEATS with queryable metadata terms, fostering potential avenues of research and facilitating the creation of highly personalized music playlists for clinical or recreational applications.</p></abstract><funding-group><funding-statement>This research was supported by the National Research Foundation (NRF; <ext-link ext-link-type="uri" xlink:href="http://www.nrf.gov.sg/">http://www.nrf.gov.sg/</ext-link>) and managed through the multi-agency Interactive & Digital Media Programme Office (IDMPO; <ext-link ext-link-type="uri" xlink:href="http://www.idm.sg/">http://www.idm.sg/</ext-link>) hosted by the Media Development Authority of Singapore (MDA; <ext-link ext-link-type="uri" xlink:href="http://www.mda.gov.sg/">http://www.mda.gov.sg/</ext-link>) under the Centre of Social Media Innovations for Communities (COSMIC; <ext-link ext-link-type="uri" xlink:href="http://cosmic.nus.edu.sg/">http://cosmic.nus.edu.sg/</ext-link>). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><page-count count="24"></page-count></counts><custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name><meta-value>The authors confirm that all data underlying the findings are fully available without restriction. All raw data were obtained from the Million Song Dataset (<ext-link ext-link-type="uri" xlink:href="http://labrosa.ee.columbia.edu/millionsong/">http://labrosa.ee.columbia.edu/millionsong/</ext-link>). All code described in the present paper is available at (<ext-link ext-link-type="uri" xlink:href="http://code.smcnus.org/">http://code.smcnus.org/</ext-link>).</meta-value></custom-meta></custom-meta-group></article-meta><notes><title>Data Availability</title><p>The authors confirm that all data underlying the findings are fully available without restriction. All raw data were obtained from the Million Song Dataset (<ext-link ext-link-type="uri" xlink:href="http://labrosa.ee.columbia.edu/millionsong/">http://labrosa.ee.columbia.edu/millionsong/</ext-link>). All code described in the present paper is available at (<ext-link ext-link-type="uri" xlink:href="http://code.smcnus.org/">http://code.smcnus.org/</ext-link>).</p></notes></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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