Towards Reliable Partial Music Alignments Using Multiple Synchronization Strategies
Identifieur interne : 001245 ( Istex/Corpus ); précédent : 001244; suivant : 001246Towards Reliable Partial Music Alignments Using Multiple Synchronization Strategies
Auteurs : Sebastian Ewert ; Meinard Müller ; Roger B. DannenbergSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
English descriptors
- Teeft :
- Algorithm, Alignment, Alignment methods, Alignment result, Alignment results, Alignment strategies, Alignment strategy, Anthology release, Audio, Audio recording, Audio recordings, Automatic synchronization, Beatles, Beatles songs, Binary matrices, Boundary condition, Chroma, Chroma features, Computer accompaniment, Consistency alignment, Corresponding area, Cost matrix, Critical passages, Dannenberg, Dynamic programming, Ewert, Exible alignment, Feature vectors, Future work, Global path, Horizontal axis, Ieee icassp, Interesting deviations, Intersection matrix, Ismir, Local cost measure, Local deviations, Markov models, Matrix, Midi, Midi segment, Midi version, Misguided alignments, Monotonic, Multiple information sources, Music alignments, Music data, Music database, Music representations, Music synchronization, Musical work, Negative entries, Negative score, Novel method, Optimal path, Other hand, Overall synchronization result, Polyphonic, Proc, Recursive, Recursive variant, Reliable passages, Saarland university, Same piece, Score alignment, Score matrix, Sect, Several techniques, Several types, Solo segments, Subsequent discussion, Subsequent improvements, Synchronization, Synchronization results, Synchronization strategies, Synchronization task, Various alignments, Vertical axis.
Abstract
Abstract: The general goal of music synchronization is to align multiple information sources related to a given piece of music. This becomes a hard problem when the various representations to be aligned reveal significant differences not only in tempo, instrumentation, or dynamics but also in structure or polyphony. Because of the complexity and diversity of music data, one can not expect to find a universal synchronization algorithm that yields reasonable solutions in all situations. In this paper, we present a novel method that allows for automatically identifying the reliable parts of alignment results. Instead of relying on one single strategy, our idea is to combine several types of conceptually different synchronization strategies within an extensible framework, thus accounting for various musical aspects. Looking for consistencies and inconsistencies across the synchronization results, our method automatically classifies the alignments locally as reliable or critical. Considering only the reliable parts yields a high-precision partial alignment. Moreover, the identification of critical parts is also useful, as they often reveal musically interesting deviations between the versions to be aligned.
Url:
DOI: 10.1007/978-3-642-18449-9_4
Links to Exploration step
ISTEX:B170F009ADEA3E056315CE14389E9BF0AB934B8ALe document en format XML
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This becomes a hard problem when the various representations to be aligned reveal significant differences not only in tempo, instrumentation, or dynamics but also in structure or polyphony. Because of the complexity and diversity of music data, one can not expect to find a universal synchronization algorithm that yields reasonable solutions in all situations. In this paper, we present a novel method that allows for automatically identifying the reliable parts of alignment results. Instead of relying on one single strategy, our idea is to combine several types of conceptually different synchronization strategies within an extensible framework, thus accounting for various musical aspects. Looking for consistencies and inconsistencies across the synchronization results, our method automatically classifies the alignments locally as reliable or critical. Considering only the reliable parts yields a high-precision partial alignment. 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Understanding Media and Adapting to the User</title><language><json:string>unknown</json:string></language><copyrightDate>2011</copyrightDate><doi><json:string>10.1007/978-3-642-18449-9</json:string></doi><issn><json:string>0302-9743</json:string></issn><eissn><json:string>1611-3349</json:string></eissn><eisbn><json:string>978-3-642-18449-9</json:string></eisbn><bookId><json:string>978-3-642-18449-9</json:string></bookId><isbn><json:string>978-3-642-18448-2</json:string></isbn><volume>6535</volume><pages><first>35</first><last>48</last></pages><genre><json:string>book-series</json:string></genre><editor><json:item><name>Marcin Detyniecki</name><affiliations><json:string>Universite Pierre et Marie Curie, Paris, France</json:string><json:string>E-mail: marcin.detyniecki@lip6.fr</json:string></affiliations></json:item><json:item><name>Ana García-Serrano</name><affiliations><json:string>Universidad Nacional de Educación a Distancia, Madrid, Spain</json:string><json:string>E-mail: agarcia@lsi.uned.es</json:string></affiliations></json:item><json:item><name>Andreas Nürnberger</name><affiliations><json:string>Otto-von-Guericke Universität Madgeburg, Magdeburg, Germany</json:string><json:string>E-mail: andreas.nuernberger@ovgu.de</json:string></affiliations></json:item></editor><subject><json:item><value>Computer Science</value></json:item><json:item><value>Computer Science</value></json:item><json:item><value>Information Systems Applications (incl.Internet)</value></json:item><json:item><value>Information Storage and Retrieval</value></json:item><json:item><value>Artificial Intelligence (incl. 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type="first">C.</forename><surname>Mitchell</surname></persName><affiliation>Stanford University, Stanford, CA, USA</affiliation></editor><editor xml:id="serie-author-0006"><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation></editor><editor xml:id="serie-author-0007"><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation>University of Bern, Bern, Switzerland</affiliation></editor><editor xml:id="serie-author-0008"><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation>Indian Institute of Technology, Madras, India</affiliation></editor><editor xml:id="serie-author-0009"><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation>University of Dortmund, Dortmund, Germany</affiliation></editor><editor xml:id="serie-author-0010"><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation></editor><editor xml:id="serie-author-0011"><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation>University of California, Los Angeles, CA, USA</affiliation></editor><editor xml:id="serie-author-0012"><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation>University of California, Berkeley, CA, USA</affiliation></editor><editor xml:id="serie-author-0013"><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation>Rice University, Houston, TX, USA</affiliation></editor><editor xml:id="serie-author-0014"><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation></editor><biblScope><date>2011</date></biblScope><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="series-Id">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2011</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: The general goal of music synchronization is to align multiple information sources related to a given piece of music. This becomes a hard problem when the various representations to be aligned reveal significant differences not only in tempo, instrumentation, or dynamics but also in structure or polyphony. Because of the complexity and diversity of music data, one can not expect to find a universal synchronization algorithm that yields reasonable solutions in all situations. In this paper, we present a novel method that allows for automatically identifying the reliable parts of alignment results. Instead of relying on one single strategy, our idea is to combine several types of conceptually different synchronization strategies within an extensible framework, thus accounting for various musical aspects. Looking for consistencies and inconsistencies across the synchronization results, our method automatically classifies the alignments locally as reliable or critical. Considering only the reliable parts yields a high-precision partial alignment. Moreover, the identification of critical parts is also useful, as they often reveal musically interesting deviations between the versions to be aligned.</p></abstract><textClass><keywords scheme="Book-Subject-Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book-Subject-Group"><list><label>I</label><item><term>Computer Science</term></item><label>I18040</label><item><term>Information Systems Applications (incl.Internet)</term></item><label>I18032</label><item><term>Information Storage and Retrieval</term></item><label>I21017</label><item><term>Artificial Intelligence (incl. 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University</OrgName><OrgAddress><City>Lancaster</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-18449-9</BookID><BookTitle>Adaptive Multimedia Retrieval. Understanding Media and Adapting to the User</BookTitle><BookSubTitle>7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009, Revised Selected Papers</BookSubTitle><BookVolumeNumber>6535</BookVolumeNumber><BookSequenceNumber>6535</BookSequenceNumber><BookDOI>10.1007/978-3-642-18449-9</BookDOI><BookTitleID>216313</BookTitleID><BookPrintISBN>978-3-642-18448-2</BookPrintISBN><BookElectronicISBN>978-3-642-18449-9</BookElectronicISBN><BookChapterCount>13</BookChapterCount><BookCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18040" Priority="1" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I18032" Priority="2" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I21017" Priority="3" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I22021" Priority="4" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I18024" Priority="5" Type="Secondary">Database Management</BookSubject><BookSubject Code="I18059" Priority="6" Type="Secondary">Multimedia Information Systems</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Marcin</GivenName><FamilyName>Detyniecki</FamilyName></EditorName><Contact><Email>marcin.detyniecki@lip6.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Ana</GivenName><FamilyName>García-Serrano</FamilyName></EditorName><Contact><Email>agarcia@lsi.uned.es</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Andreas</GivenName><FamilyName>Nürnberger</FamilyName></EditorName><Contact><Email>andreas.nuernberger@ovgu.de</Email></Contact></Editor><Affiliation ID="Aff16"><OrgName>Universite Pierre et Marie Curie</OrgName><OrgAddress><City>Paris</City><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>Universidad Nacional de Educación a Distancia</OrgName><OrgAddress><City>Madrid</City><Country>Spain</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>Otto-von-Guericke Universität Madgeburg</OrgName><OrgAddress><City>Magdeburg</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part3"><PartInfo TocLevels="0"><PartID>3</PartID><PartSequenceNumber>3</PartSequenceNumber><PartTitle>Pinpointing Music</PartTitle><PartChapterCount>2</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Adaptive Multimedia Retrieval</BookTitle></PartContext></PartInfo><Chapter ID="Chap4" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>4</ChapterID><ChapterDOI>10.1007/978-3-642-18449-9_4</ChapterDOI><ChapterSequenceNumber>4</ChapterSequenceNumber><ChapterTitle Language="En">Towards Reliable Partial Music Alignments Using Multiple Synchronization Strategies</ChapterTitle><ChapterFirstPage>35</ChapterFirstPage><ChapterLastPage>48</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>3</PartID><BookID>978-3-642-18449-9</BookID><BookTitle>Adaptive Multimedia Retrieval</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Sebastian</GivenName><FamilyName>Ewert</FamilyName></AuthorName><Contact><Email>ewerts@iai.uni-bonn.de</Email></Contact></Author><Author AffiliationIDS="Aff20 Aff21"><AuthorName DisplayOrder="Western"><GivenName>Meinard</GivenName><FamilyName>Müller</FamilyName></AuthorName><Contact><Email>meinard@mpi-inf.mpg.de</Email></Contact></Author><Author AffiliationIDS="Aff22"><AuthorName DisplayOrder="Western"><GivenName>Roger</GivenName><GivenName>B.</GivenName><FamilyName>Dannenberg</FamilyName></AuthorName><Contact><Email>rbd@cs.cmu.edu</Email></Contact></Author><Affiliation ID="Aff19"><OrgDivision>Multimedia Signal Processing Group</OrgDivision><OrgName>Bonn University</OrgName><OrgAddress><City>Bonn</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgName>Saarland University</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff21"><OrgName>MPI Informatik</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff22"><OrgDivision>School of Computer Science</OrgDivision><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>The general goal of music synchronization is to align multiple information sources related to a given piece of music. This becomes a hard problem when the various representations to be aligned reveal significant differences not only in tempo, instrumentation, or dynamics but also in structure or polyphony. Because of the complexity and diversity of music data, one can not expect to find a universal synchronization algorithm that yields reasonable solutions in all situations. In this paper, we present a novel method that allows for automatically identifying the reliable parts of alignment results. Instead of relying on one single strategy, our idea is to combine several types of conceptually different synchronization strategies within an extensible framework, thus accounting for various musical aspects. Looking for consistencies and inconsistencies across the synchronization results, our method automatically classifies the alignments locally as reliable or critical. Considering only the reliable parts yields a high-precision partial alignment. Moreover, the identification of critical parts is also useful, as they often reveal musically interesting deviations between the versions to be aligned.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Towards Reliable Partial Music Alignments Using Multiple Synchronization Strategies</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Towards Reliable Partial Music Alignments Using Multiple Synchronization Strategies</title></titleInfo><name type="personal"><namePart type="given">Sebastian</namePart><namePart type="family">Ewert</namePart><affiliation>Multimedia Signal Processing Group, Bonn University, Bonn, Germany</affiliation><affiliation>E-mail: ewerts@iai.uni-bonn.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Meinard</namePart><namePart type="family">Müller</namePart><affiliation>Saarland University, Saarbrücken, Germany</affiliation><affiliation>MPI Informatik, Saarbrücken, Germany</affiliation><affiliation>E-mail: meinard@mpi-inf.mpg.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Roger</namePart><namePart type="given">B.</namePart><namePart type="family">Dannenberg</namePart><affiliation>School of Computer Science, Carnegie Mellon University, Pittsburgh, USA</affiliation><affiliation>E-mail: rbd@cs.cmu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: The general goal of music synchronization is to align multiple information sources related to a given piece of music. This becomes a hard problem when the various representations to be aligned reveal significant differences not only in tempo, instrumentation, or dynamics but also in structure or polyphony. Because of the complexity and diversity of music data, one can not expect to find a universal synchronization algorithm that yields reasonable solutions in all situations. In this paper, we present a novel method that allows for automatically identifying the reliable parts of alignment results. Instead of relying on one single strategy, our idea is to combine several types of conceptually different synchronization strategies within an extensible framework, thus accounting for various musical aspects. Looking for consistencies and inconsistencies across the synchronization results, our method automatically classifies the alignments locally as reliable or critical. Considering only the reliable parts yields a high-precision partial alignment. 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type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name 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