Real Time Tracking and Visualisation of Musical Expression
Identifieur interne : 000910 ( Istex/Corpus ); précédent : 000909; suivant : 000911Real Time Tracking and Visualisation of Musical Expression
Auteurs : Simon Dixon ; Werner Goebl ; Gerhard WidmerSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: Skilled musicians are able to shape a given piece of music (by continuously modulating aspects like tempo, loudness, etc.) to communicate high level information such as musical structure and emotion. This activity is commonly referred to as expressive music performance. The present paper presents another step towards the automatic high-level analysis of this elusive phenomenon with AI methods. A system is presented that is able to measure tempo and dynamics of a musical performance and to track their development over time. The system accepts raw audio input, tracks tempo and dynamics changes in real time, and displays the development of these expressive parameters in an intuitive and aesthetically appealing graphical format which provides insight into the expressive patterns applied by skilled artists. The paper describes the tempo tracking algorithm (based on a new clustering method) in detail, and then presents an application of the system to the analysis of performances by different pianists.
Url:
DOI: 10.1007/3-540-45722-4_7
Links to Exploration step
ISTEX:97746F9B96FB403E095C6FC9D9CEC06341D27891Le document en format XML
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This activity is commonly referred to as expressive music performance. The present paper presents another step towards the automatic high-level analysis of this elusive phenomenon with AI methods. A system is presented that is able to measure tempo and dynamics of a musical performance and to track their development over time. The system accepts raw audio input, tracks tempo and dynamics changes in real time, and displays the development of these expressive parameters in an intuitive and aesthetically appealing graphical format which provides insight into the expressive patterns applied by skilled artists. 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type="first">Jörg</forename><surname>Siekmann</surname></persName><affiliation>University of Saarland, Saarbrücken, Germany</affiliation></editor><biblScope type="seriesId">1244</biblScope></series><idno type="istex">97746F9B96FB403E095C6FC9D9CEC06341D27891</idno><idno type="DOI">10.1007/3-540-45722-4_7</idno><idno type="ChapterID">Chap7</idno><idno type="ChapterID">7</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2002</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: Skilled musicians are able to shape a given piece of music (by continuously modulating aspects like tempo, loudness, etc.) to communicate high level information such as musical structure and emotion. This activity is commonly referred to as expressive music performance. The present paper presents another step towards the automatic high-level analysis of this elusive phenomenon with AI methods. A system is presented that is able to measure tempo and dynamics of a musical performance and to track their development over time. The system accepts raw audio input, tracks tempo and dynamics changes in real time, and displays the development of these expressive parameters in an intuitive and aesthetically appealing graphical format which provides insight into the expressive patterns applied by skilled artists. 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Robotics)</BookSubject><BookSubject Code="I23036" Priority="2" Type="Secondary">Computer Appl. in Arts and Humanities</BookSubject><BookSubject Code="I2203X" Priority="3" Type="Secondary">Pattern Recognition</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff1"><EditorName DisplayOrder="Western"><GivenName>Christina</GivenName><FamilyName>Anagnostopoulou</FamilyName></EditorName><Contact><Email>chrisa@dai.ed.ac.uk</Email></Contact></Editor><Editor AffiliationIDS="Aff1"><EditorName DisplayOrder="Western"><GivenName>Miguel</GivenName><FamilyName>Ferrand</FamilyName></EditorName><Contact><Email>mferrand@music.ed.ac.uk</Email></Contact></Editor><Editor AffiliationIDS="Aff2"><EditorName DisplayOrder="Western"><GivenName>Alan</GivenName><FamilyName>Smaill</FamilyName></EditorName><Contact><Email>a.smaill@ed.ac.uk</Email></Contact></Editor><Affiliation ID="Aff1"><OrgDivision>Faculty of Music</OrgDivision><OrgName>University of Edinburgh</OrgName><OrgAddress><Street>12 Nicolson Square</Street><Postcode>EH8 9DF</Postcode><City>Edinburgh</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Division of Informatics</OrgDivision><OrgName>University of Edinburgh</OrgName><OrgAddress><Street>80 South Bridge</Street><Postcode>EH1 1HN</Postcode><City>Edinburgh</City><Country>UK</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part2"><PartInfo TocLevels="0"><PartID>2</PartID><PartSequenceNumber>2</PartSequenceNumber><PartTitle>Regular Contributions</PartTitle><PartChapterCount>16</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookID>3-540-45722-4</BookID><BookTitle>Music and Artificial Intelligence</BookTitle></PartContext></PartInfo><Chapter ID="Chap7" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" Language="En" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>7</ChapterID><ChapterDOI>10.1007/3-540-45722-4_7</ChapterDOI><ChapterSequenceNumber>7</ChapterSequenceNumber><ChapterTitle Language="En">Real Time Tracking and Visualisation of Musical Expression</ChapterTitle><ChapterFirstPage>58</ChapterFirstPage><ChapterLastPage>68</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2002</CopyrightYear></ChapterCopyright><ChapterHistory><RegistrationDate><Year>2002</Year><Month>8</Month><Day>26</Day></RegistrationDate><OnlineDate><Year>2002</Year><Month>8</Month><Day>27</Day></OnlineDate></ChapterHistory><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>2</PartID><BookID>3-540-45722-4</BookID><BookTitle>Music and Artificial Intelligence</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Simon</GivenName><FamilyName>Dixon</FamilyName></AuthorName><Contact><Email>simon@ai.univie.ac.at</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Werner</GivenName><FamilyName>Goebl</FamilyName></AuthorName><Contact><Email>wernerg@ai.univie.ac.at</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Widmer</FamilyName></AuthorName><Contact><Email>gerhard@ai.univie.ac.at</Email></Contact></Author><Affiliation ID="Aff5"><OrgName>Austrian Research Institute for Artificial Intelligence</OrgName><OrgAddress><Street>Schottengasse 3</Street><Postcode>A-1010</Postcode><City>Vienna</City><Country>Austria</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Skilled musicians are able to shape a given piece of music (by continuously modulating aspects like tempo, loudness, etc.) to communicate high level information such as musical structure and emotion. This activity is commonly referred to as expressive music performance. The present paper presents another step towards the automatic high-level analysis of this elusive phenomenon with AI methods. A system is presented that is able to measure tempo and dynamics of a musical performance and to track their development over time. The system accepts raw audio input, tracks tempo and dynamics changes in real time, and displays the development of these expressive parameters in an intuitive and aesthetically appealing graphical format which provides insight into the expressive patterns applied by skilled artists. The paper describes the tempo tracking algorithm (based on a new clustering method) in detail, and then presents an application of the system to the analysis of performances by different pianists.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle><SubSeriesSubTitle Language="En">Subseries of Lecture Notes in Computer Science</SubSeriesSubTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff3"><EditorName DisplayOrder="Western"><GivenName>Jaime</GivenName><GivenName>G.</GivenName><FamilyName>Carbonell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff4"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Affiliation ID="Aff3"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Real Time Tracking and Visualisation of Musical Expression</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Real Time Tracking and Visualisation of Musical Expression</title></titleInfo><name type="personal"><namePart type="given">Simon</namePart><namePart type="family">Dixon</namePart><affiliation>Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria</affiliation><affiliation>E-mail: simon@ai.univie.ac.at</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Werner</namePart><namePart type="family">Goebl</namePart><affiliation>Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria</affiliation><affiliation>E-mail: wernerg@ai.univie.ac.at</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Widmer</namePart><affiliation>Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria</affiliation><affiliation>E-mail: gerhard@ai.univie.ac.at</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">2002</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: Skilled musicians are able to shape a given piece of music (by continuously modulating aspects like tempo, loudness, etc.) to communicate high level information such as musical structure and emotion. This activity is commonly referred to as expressive music performance. The present paper presents another step towards the automatic high-level analysis of this elusive phenomenon with AI methods. A system is presented that is able to measure tempo and dynamics of a musical performance and to track their development over time. The system accepts raw audio input, tracks tempo and dynamics changes in real time, and displays the development of these expressive parameters in an intuitive and aesthetically appealing graphical format which provides insight into the expressive patterns applied by skilled artists. The paper describes the tempo tracking algorithm (based on a new clustering method) in detail, and then presents an application of the system to the analysis of performances by different pianists.</abstract><relatedItem type="host"><titleInfo><title>Music and Artificial Intelligence</title><subTitle>Second International Conference, ICMAI 2002 Edinburgh, Scotland, UK, September 12–14, 2002 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Christina</namePart><namePart type="family">Anagnostopoulou</namePart><affiliation>Faculty of Music, University of Edinburgh, 12 Nicolson Square, EH8 9DF, Edinburgh, UK</affiliation><affiliation>E-mail: chrisa@dai.ed.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Miguel</namePart><namePart type="family">Ferrand</namePart><affiliation>Faculty of Music, University of Edinburgh, 12 Nicolson Square, EH8 9DF, Edinburgh, UK</affiliation><affiliation>E-mail: mferrand@music.ed.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Alan</namePart><namePart type="family">Smaill</namePart><affiliation>Division of Informatics, University of Edinburgh, 80 South Bridge, EH1 1HN, Edinburgh, UK</affiliation><affiliation>E-mail: a.smaill@ed.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book series">Proceedings</genre><originInfo><copyrightDate encoding="w3cdtf">2002</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="I21017">Artificial Intelligence (incl. 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type="given">Gerhard</namePart><namePart type="family">Goos</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Juris</namePart><namePart type="family">Hartmanis</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jan</namePart><namePart type="family">van Leeuwen</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Christina</namePart><namePart type="family">Anagnostopoulou</namePart><affiliation>Faculty of Music, University of Edinburgh, 12 Nicolson Square, EH8 9DF, Edinburgh, UK</affiliation><affiliation>E-mail: chrisa@dai.ed.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Miguel</namePart><namePart type="family">Ferrand</namePart><affiliation>Faculty of Music, University of Edinburgh, 12 Nicolson Square, EH8 9DF, Edinburgh, 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