Unsupervised Analysis and Generation of Audio Percussion Sequences
Identifieur interne : 001096 ( Istex/Corpus ); précédent : 001095; suivant : 001097Unsupervised Analysis and Generation of Audio Percussion Sequences
Auteurs : Marco Marchini ; Hendrik PurwinsSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
English descriptors
Abstract
Abstract: A system is presented that learns the structure of an audio recording of a rhythmical percussion fragment in an unsupervised manner and that synthesizes musical variations from it. The procedure consists of 1) segmentation, 2) symbolization (feature extraction, clustering, sequence structure analysis, temporal alignment), and 3) synthesis. The symbolization step yields a sequence of event classes. Simultaneously, representations are maintained that cluster the events into few or many classes. Based on the most regular clustering level, a tempo estimation procedure is used to preserve the metrical structure in the generated sequence. Employing variable length Markov chains, the final synthesis is performed, recombining the audio material derived from the sample itself. Representations with different numbers of classes are used to trade off statistical significance (short context sequence, low clustering refinement) versus specificity (long context, high clustering refinement) of the generated sequence. For a broad variety of musical styles the musical characteristics of the original are preserved. At the same time, considerable variability is introduced in the generated sequence.
Url:
DOI: 10.1007/978-3-642-23126-1_14
Links to Exploration step
ISTEX:8E69B0150B067CF0ABF55E0DF3EB0AED1CEAD0C9Le document en format XML
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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>978-3-642-23126-1</BookID><BookTitle>Exploring Music Contents</BookTitle><BookSubTitle>7th International Symposium, CMMR 2010, Málaga, Spain, June 21-24, 2010. Revised Papers</BookSubTitle><BookVolumeNumber>6684</BookVolumeNumber><BookSequenceNumber>6684</BookSequenceNumber><BookDOI>10.1007/978-3-642-23126-1</BookDOI><BookTitleID>272309</BookTitleID><BookPrintISBN>978-3-642-23125-4</BookPrintISBN><BookElectronicISBN>978-3-642-23126-1</BookElectronicISBN><BookChapterCount>22</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag GmbH Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18032" Priority="1" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="V32000" Priority="2" Type="Secondary">Music</BookSubject><BookSubject Code="I18059" Priority="3" Type="Secondary">Multimedia Information Systems</BookSubject><BookSubject Code="I23036" Priority="4" Type="Secondary">Computer Appl. in Arts and Humanities</BookSubject><BookSubject Code="I18067" Priority="5" Type="Secondary">User Interfaces and Human Computer Interaction</BookSubject><BookSubject Code="I13030" Priority="6" Type="Secondary">Special Purpose and Application-Based Systems</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Sølvi</GivenName><FamilyName>Ystad</FamilyName></EditorName><Contact><Email>ystad@lma.cnrs-mrs.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Mitsuko</GivenName><FamilyName>Aramaki</FamilyName></EditorName><Contact><Email>aramaki@incm.cnrs-mrs.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Richard</GivenName><FamilyName>Kronland-Martinet</FamilyName></EditorName><Contact><Email>kronland@lma.cnrs-mrs.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Kristoffer</GivenName><FamilyName>Jensen</FamilyName></EditorName><Contact><Email>krist@create.aau.dk</Email></Contact></Editor><Affiliation ID="Aff16"><OrgName>CNRS - LMA</OrgName><OrgAddress><Street>31 Chemin Joseph Aiguier</Street><Postcode>13402</Postcode><City>Marseille Cedex 20</City><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>CNRS-INCM</OrgName><OrgAddress><Street>31 Chemin Joseph Aiguier</Street><Postcode>13402</Postcode><City>Marseille Cedex 20</City><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>CNRS-LMA</OrgName><OrgAddress><Street>31 Chemin Joseph Aiguier</Street><Postcode>13402</Postcode><City>Marseille Cedex 20</City><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgName>Aalborg University Esbjerg</OrgName><OrgAddress><Street>Niels Bohr Vej 8</Street><Postcode>6700</Postcode><City>Esbjerg</City><Country>Denmark</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part4"><PartInfo TocLevels="0"><PartID>4</PartID><PartNumber>Part IV</PartNumber><PartSequenceNumber>4</PartSequenceNumber><PartTitle>Analysis and Data Mining</PartTitle><PartChapterCount>4</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Exploring Music Contents</BookTitle></PartContext></PartInfo><Chapter ID="Chap14" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>14</ChapterID><ChapterDOI>10.1007/978-3-642-23126-1_14</ChapterDOI><ChapterSequenceNumber>14</ChapterSequenceNumber><ChapterTitle Language="En">Unsupervised Analysis and Generation of Audio Percussion Sequences</ChapterTitle><ChapterFirstPage>205</ChapterFirstPage><ChapterLastPage>218</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>4</PartID><BookID>978-3-642-23126-1</BookID><BookTitle>Exploring Music Contents</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Marco</GivenName><FamilyName>Marchini</FamilyName></AuthorName><Contact><Email>marco.marchini@upf.edu</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Hendrik</GivenName><FamilyName>Purwins</FamilyName></AuthorName><Contact><Email>hendrik.purwins@upf.edu</Email></Contact></Author><Affiliation ID="Aff20"><OrgDivision>Music Technology Group, Department of Information and Communications Technologies</OrgDivision><OrgName>Universitat Pompeu Fabra</OrgName><OrgAddress><Street>Roc Boronat, 138</Street><Postcode>08018</Postcode><City>Barcelona</City><Country>Spain</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>A system is presented that learns the structure of an audio recording of a rhythmical percussion fragment in an unsupervised manner and that synthesizes musical variations from it. The procedure consists of 1) segmentation, 2) symbolization (feature extraction, clustering, sequence structure analysis, temporal alignment), and 3) synthesis. The symbolization step yields a sequence of event classes. Simultaneously, representations are maintained that cluster the events into few or many classes. Based on the most regular clustering level, a tempo estimation procedure is used to preserve the metrical structure in the generated sequence. Employing variable length Markov chains, the final synthesis is performed, recombining the audio material derived from the sample itself. Representations with different numbers of classes are used to trade off statistical significance (short context sequence, low clustering refinement) versus specificity (long context, high clustering refinement) of the generated sequence. For a broad variety of musical styles the musical characteristics of the original are preserved. At the same time, considerable variability is introduced in the generated sequence.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>music analysis</Keyword><Keyword>music generation</Keyword><Keyword>unsupervised clustering</Keyword><Keyword>Markov chains</Keyword><Keyword>machine listening</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Unsupervised Analysis and Generation of Audio Percussion Sequences</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Unsupervised Analysis and Generation of Audio Percussion Sequences</title></titleInfo><name type="personal"><namePart type="given">Marco</namePart><namePart type="family">Marchini</namePart><affiliation>Music Technology Group, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Roc Boronat, 138, 08018, Barcelona, Spain</affiliation><affiliation>E-mail: marco.marchini@upf.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Hendrik</namePart><namePart type="family">Purwins</namePart><affiliation>Music Technology Group, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Roc Boronat, 138, 08018, Barcelona, Spain</affiliation><affiliation>E-mail: hendrik.purwins@upf.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="Book Chapter">OriginalPaper</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><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: A system is presented that learns the structure of an audio recording of a rhythmical percussion fragment in an unsupervised manner and that synthesizes musical variations from it. The procedure consists of 1) segmentation, 2) symbolization (feature extraction, clustering, sequence structure analysis, temporal alignment), and 3) synthesis. The symbolization step yields a sequence of event classes. Simultaneously, representations are maintained that cluster the events into few or many classes. Based on the most regular clustering level, a tempo estimation procedure is used to preserve the metrical structure in the generated sequence. Employing variable length Markov chains, the final synthesis is performed, recombining the audio material derived from the sample itself. Representations with different numbers of classes are used to trade off statistical significance (short context sequence, low clustering refinement) versus specificity (long context, high clustering refinement) of the generated sequence. For a broad variety of musical styles the musical characteristics of the original are preserved. At the same time, considerable variability is introduced in the generated sequence.</abstract><subject lang="en"><genre>Keywords</genre><topic>music analysis</topic><topic>music generation</topic><topic>unsupervised clustering</topic><topic>Markov chains</topic><topic>machine listening</topic></subject><relatedItem type="host"><titleInfo><title>Exploring Music Contents</title><subTitle>7th International Symposium, CMMR 2010, Málaga, Spain, June 21-24, 2010. Revised Papers</subTitle></titleInfo><name type="personal"><namePart type="given">Sølvi</namePart><namePart type="family">Ystad</namePart><affiliation>CNRS - LMA, 31 Chemin Joseph Aiguier, 13402, Marseille Cedex 20, France</affiliation><affiliation>E-mail: ystad@lma.cnrs-mrs.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Mitsuko</namePart><namePart type="family">Aramaki</namePart><affiliation>CNRS-INCM, 31 Chemin Joseph Aiguier, 13402, Marseille Cedex 20, France</affiliation><affiliation>E-mail: aramaki@incm.cnrs-mrs.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Richard</namePart><namePart type="family">Kronland-Martinet</namePart><affiliation>CNRS-LMA, 31 Chemin Joseph Aiguier, 13402, Marseille Cedex 20, France</affiliation><affiliation>E-mail: kronland@lma.cnrs-mrs.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Kristoffer</namePart><namePart type="family">Jensen</namePart><affiliation>Aalborg University Esbjerg, Niels Bohr Vej 8, 6700, Esbjerg, Denmark</affiliation><affiliation>E-mail: krist@create.aau.dk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book series">Proceedings</genre><originInfo><copyrightDate encoding="w3cdtf">2011</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="V32000">Music</topic><topic authority="SpringerSubjectCodes" authorityURI="I18059">Multimedia Information Systems</topic><topic authority="SpringerSubjectCodes" authorityURI="I23036">Computer Appl. in Arts and Humanities</topic><topic authority="SpringerSubjectCodes" authorityURI="I18067">User Interfaces and Human Computer Interaction</topic><topic authority="SpringerSubjectCodes" authorityURI="I13030">Special Purpose and Application-Based Systems</topic></subject><identifier type="DOI">10.1007/978-3-642-23126-1</identifier><identifier type="ISBN">978-3-642-23125-4</identifier><identifier type="eISBN">978-3-642-23126-1</identifier><identifier type="BookTitleID">272309</identifier><identifier type="BookID">978-3-642-23126-1</identifier><identifier type="BookChapterCount">22</identifier><identifier type="BookVolumeNumber">6684</identifier><identifier type="BookSequenceNumber">6684</identifier><identifier type="PartChapterCount">4</identifier><part><date>2011</date><detail type="part"><title>Part IV: Analysis and Data Mining</title></detail><detail 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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 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|étape= Corpus
|type= RBID
|clé= ISTEX:8E69B0150B067CF0ABF55E0DF3EB0AED1CEAD0C9
|texte= Unsupervised Analysis and Generation of Audio Percussion Sequences
}}
| This area was generated with Dilib version V0.6.20. |  |