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Dagstuhl Seminar Proceedings, Volume 9051

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  • published at: 2009-04-02
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
DOI: 10.4230/DagSemProc.09051.1
09051 Abstracts Collection – Knowledge representation for intelligent music processing

Authors: Eleanor Selfridge-Field, Frans Wiering, and Geraint A. Wiggins

Abstract
From the twenty-fifth to the thirtieth of January, 2009, the Dagstuhl Seminar 09051 on ``Knowledge representation for intelligent music processing'' was held in Schloss Dagstuhl~--~Leibniz Centre for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations and demos given during the seminar as well as plenary presentations, reports of workshop discussions, results and ideas are put together in this paper. The first section describes the seminar topics and goals in general, followed by plenary `stimulus' papers, followed by reports and abstracts arranged by workshop followed finally by some concluding materials providing views of both the seminar itself and also forward to the longer-term goals of the discipline. Links to extended abstracts, full papers and supporting materials are provided, if available. The organisers thank David Lewis for editing these proceedings.
Cite as

Eleanor Selfridge-Field, Frans Wiering, and Geraint A. Wiggins. 09051 Abstracts Collection – Knowledge representation for intelligent music processing. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{selfridgefield_et_al:DagSemProc.09051.1,
  author =	{Selfridge-Field, Eleanor and Wiering, Frans and Wiggins, Geraint A.},
  title =	{{09051 Abstracts Collection – Knowledge representation for intelligent music processing}},
  booktitle =	{Knowledge representation for intelligent music processing},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.1},
  URN =		{urn:nbn:de:0030-drops-19722},
  doi =		{10.4230/DagSemProc.09051.1},
  annote =	{Keywords: Music representation, music encoding, digital music edition, Music Information Retrieval, intelligent music processing, music informatics, data formats, data interchange, music collections, audio, MIDI, MEI, TEI, humdrum}
}
Document
DOI: 10.4230/DagSemProc.09051.2
Ambiguity and Multiplicity in Music Representation

Authors: Alan Marsden

Abstract
Proper representation of music must often be ambiguous and/or multiple. I give examples of this and propose some high-level strategies.
Cite as

Alan Marsden. Ambiguity and Multiplicity in Music Representation. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{marsden:DagSemProc.09051.2,
  author =	{Marsden, Alan},
  title =	{{Ambiguity and Multiplicity in Music Representation}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.2},
  URN =		{urn:nbn:de:0030-drops-19694},
  doi =		{10.4230/DagSemProc.09051.2},
  annote =	{Keywords: Music Representation}
}
Document
DOI: 10.4230/DagSemProc.09051.3
Case Study ``Beatles Songs'' – What can be Learned from Unreliable Music Alignments?

Authors: Sebastian Ewert, Meinard Müller, Daniel Müllensiefen, Michael Clausen, and Geraint A. Wiggins

Abstract
As a result of massive digitization efforts and the world wide web, there is an exploding amount of available digital data describing and representing music at various semantic levels and in diverse formats. For example, in the case of the Beatles songs, there are numerous recordings including an increasing number of cover songs and arrangements as well as MIDI data and other symbolic music representations. The general goal of music synchronization is to align the multiple information sources related to a given piece of music. This becomes a difficult problem when the various representations reveal significant differences in structure and polyphony, while exhibiting various types of artifacts. In this paper, we address the issue of how music synchronization techniques are useful for automatically revealing critical passages with significant difference between the two versions to be aligned. Using the corpus of the Beatles songs as test bed, we analyze the kind of differences occurring in audio and MIDI versions available for the songs.
Cite as

Sebastian Ewert, Meinard Müller, Daniel Müllensiefen, Michael Clausen, and Geraint A. Wiggins. Case Study ``Beatles Songs'' – What can be Learned from Unreliable Music Alignments?. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{ewert_et_al:DagSemProc.09051.3,
  author =	{Ewert, Sebastian and M\"{u}ller, Meinard and M\"{u}llensiefen, Daniel and Clausen, Michael and Wiggins, Geraint A.},
  title =	{{Case Study ``Beatles Songs'' – What can be Learned from Unreliable Music Alignments?}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.3},
  URN =		{urn:nbn:de:0030-drops-19640},
  doi =		{10.4230/DagSemProc.09051.3},
  annote =	{Keywords: MIDI, audio, music synchronization, multimodal, music collections, Beatles songs}
}
Document
DOI: 10.4230/DagSemProc.09051.4
Motivic Pattern Mining

Authors: Olivier Lartillot

Abstract
This paper presents a concise overview of a research project dedicated to Motivic Pattern Mining, i.e., the automatic discovery of motives within pieces of music through a search for repetitions in score representations.
Cite as

Olivier Lartillot. Motivic Pattern Mining. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{lartillot:DagSemProc.09051.4,
  author =	{Lartillot, Olivier},
  title =	{{Motivic Pattern Mining}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.4},
  URN =		{urn:nbn:de:0030-drops-19682},
  doi =		{10.4230/DagSemProc.09051.4},
  annote =	{Keywords: Pattern mining, motivic analysis, multidimensionality, closed patterns, cyclic patterns}
}
Document
DOI: 10.4230/DagSemProc.09051.5
Studying Music is Difficult and Important: Challenges of Music Knowledge Representation

Authors: Donald Byrd

Abstract
* Music is an art, so many musicians try to use its elements in interesting and original ways, not standardized and ordinary ways. (cf. Collins 2006) * Music is a performing art, so we have both performances and symbolic representations (both scores and transcriptions of performances). * Much music, especially Western, has synchronization requirements of a complexity equalled in no presentation of information for human consumption – art form or other -- we are aware of. * Music involves many different instruments, often in groups. No other art form we know of has anything like this, and it opens up the possibility of versions of a given work for other ensembles or at other levels of technical demands. * Music is often combined with text. * Music is extremely popular, so, for many works, numerous versions actually exist. For all these reasons, music is uniquely difficult, and uniquely valuable, to deal with -- especially by computer. To support the argument, we give examples in the form of conventional Western music notation that either violate – in several cases, blatantly – the supposed rules of music notation, or that bring up difficult issues of music representation (see Byrd 1994 and Byrd 2009). We also give examples in audio form from some unpublished work of ours to point out the astounding range of what is considered music by one culture or another. References Byrd, Donald (1994). Music Notation Software and Intelligence. Computer Music Journal 18(1), pp. 17-20; available (in scanned form) at http://www.informatics.indiana.edu/donbyrd/Papers/MusNotSoftware+Intelligence.pdf . Byrd, Donald (2009). Gallery of Interesting Music Notation. Available at http://www.informatics.indiana.edu/donbyrd/InterestingMusicNotation.html . Collins, Nick (2006, Winter). Composing to Subvert Content Retrieval Engines. ICMA Array, Winter 2006, pp. 37-41.
Cite as

Donald Byrd. Studying Music is Difficult and Important: Challenges of Music Knowledge Representation. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{byrd:DagSemProc.09051.5,
  author =	{Byrd, Donald},
  title =	{{Studying Music is Difficult and Important: Challenges of Music Knowledge Representation}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.5},
  URN =		{urn:nbn:de:0030-drops-19873},
  doi =		{10.4230/DagSemProc.09051.5},
  annote =	{Keywords: Music computing, representation}
}
Document
DOI: 10.4230/DagSemProc.09051.6
The Dagstuhl Core

Authors: Theodor Dumitrescu, Johannes Kepper, Andreas Kornstädt, Daniel Röwenstruck, Perry Roland, Craig Sapp, and Eleanor Selfridge-Field

Abstract
A substantial number of workshop attendees were involved in an extensive discussion of musical features for which software support is desirable in the context of scholarly research and applications (printing, analysis, editing of virtual materials, and other activities). Some ability to interchange data among applications is also highly desired. At the present time (early 2009) two XML descriptions for music have been extensively discussed at other meetings and workshops. These are MusicXML (commercial) and the Music Encoding Initiative (MEI; non-commercial). Over continuing discussion in ensuing days, a list of ``core'' features was developed by a group including Ted Dumitrescu, Johannes Kepper, Andreas Kornstaedt, Daniel Roewenstrunk, Perry Roland, and Eleanor Selfridge-Field. Additional input was received from Craig Sapp, who has translated extensively among four of the five data representation schemes (Humdrum Kern, MuseData, SCORE, and MusicXML) compared on the ``core'' feature list. (At the present time, no software to implement MEI is available.) The representation schemes were chosen because of the extensive repositories of music that already exist in them. The list is a work-in-progress. A wiki (restricted access) has been set up at http://muwimedial.de/dagstuhl-core/. After a short phase of refinement, it will be made available more widely. The Dagstuhl Core is a means to facilitate the use of existing and creation of new polyphonic CWN corpora (1650-1935) by educating users about the possibilities and limitations of graphemic, application- independent music data formats and the quality of programs that convert between them. It provides a feature list for each format/converter containing ``Yes''/``No''/``by Extension'' with a concise description how the given feature can (not) be realised in a given format / by a given converter. Serving as a frame of reference for parties interested in using encoded music, as well as creating corpora, formats, and converters, the vast majority of Dagstuhl Core features should carry a ``Yes'' or ``by Extension'' for a music format to be considered a core format for representing music in CWN from 1650 to 1935. The emphasis of the ``core'' is intended to be less on completeness than on ``essential features'' that any representation approach intended for use in scholarly work should be able to handle. It is currently focused on European classical music of the eighteenth and nineteenth centuries.
Cite as

Theodor Dumitrescu, Johannes Kepper, Andreas Kornstädt, Daniel Röwenstruck, Perry Roland, Craig Sapp, and Eleanor Selfridge-Field. The Dagstuhl Core. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{dumitrescu_et_al:DagSemProc.09051.6,
  author =	{Dumitrescu, Theodor and Kepper, Johannes and Kornst\"{a}dt, Andreas and R\"{o}wenstruck, Daniel and Roland, Perry and Sapp, Craig and Selfridge-Field, Eleanor},
  title =	{{The Dagstuhl Core}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--1},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.6},
  URN =		{urn:nbn:de:0030-drops-19716},
  doi =		{10.4230/DagSemProc.09051.6},
  annote =	{Keywords: Music encoding, music interchange formats}
}
Document
DOI: 10.4230/DagSemProc.09051.7
Towards Automated Processing of Folk Song Recordings

Authors: Meinard Müller, Peter Grosche, and Frans Wiering

Abstract
Folk music is closely related to the musical culture of a specific nation or region. Even though folk songs have been passed down mainly by oral tradition, most musicologists study the relation between folk songs on the basis of symbolic music descriptions, which are obtained by transcribing recorded tunes into a score-like representation. Due to the complexity of audio recordings, once having the transcriptions, the original recorded tunes are often no longer used in the actual folk song research even though they still may contain valuable information. In this paper, we present various techniques for making audio recordings more easily accessible for music researchers. In particular, we show how one can use synchronization techniques to automatically segment and annotate the recorded songs. The processed audio recordings can then be made accessible along with a symbolic transcript by means of suitable visualization, searching, and navigation interfaces to assist folk song researchers to conduct large scale investigations comprising the audio material.
Cite as

Meinard Müller, Peter Grosche, and Frans Wiering. Towards Automated Processing of Folk Song Recordings. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{muller_et_al:DagSemProc.09051.7,
  author =	{M\"{u}ller, Meinard and Grosche, Peter and Wiering, Frans},
  title =	{{Towards Automated Processing of Folk Song Recordings}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.7},
  URN =		{urn:nbn:de:0030-drops-19666},
  doi =		{10.4230/DagSemProc.09051.7},
  annote =	{Keywords: Folk songs, audio, segmentation, music synchronization, annotation, performance analysis}
}
Document
DOI: 10.4230/DagSemProc.09051.8
Towards Bridging the Gap between Sheet Music and Audio

Authors: Christian Fremerey, Meinard Mueller, and Michael Clausen

Abstract
Sheet music and audio recordings represent and describe music on different semantic levels. Sheet music describes abstract high-level parameters such as notes, keys, measures, or repeats in a visual form. Because of its explicitness and compactness, most musicologists discuss and analyze the meaning of music on the basis of sheet music. On the contrary, most people enjoy music by listening to audio recordings, which represent music in an acoustic form. In particular, the nuances and subtleties of musical performances, which are generally not written down in the score, make the music come alive. In this paper, we address the problem of bridging the gap between the sheet music domain and the audio domain. In particular, we discuss aspects on music representations, music synchronization, and optical music recognition, while indicating various strategies and open research problems.
Cite as

Christian Fremerey, Meinard Mueller, and Michael Clausen. Towards Bridging the Gap between Sheet Music and Audio. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{fremerey_et_al:DagSemProc.09051.8,
  author =	{Fremerey, Christian and Mueller, Meinard and Clausen, Michael},
  title =	{{Towards Bridging the Gap between Sheet Music and Audio}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.8},
  URN =		{urn:nbn:de:0030-drops-19651},
  doi =		{10.4230/DagSemProc.09051.8},
  annote =	{Keywords: Audio, sheet music, symbolic score, optical music recognition, music synchronization}
}

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  • B
  • Byrd, Donald
  • C
  • Clausen, Michael
  • D
  • Dumitrescu, Theodor
  • E
  • Ewert, Sebastian
  • F
  • Fremerey, Christian
  • G
  • Grosche, Peter
  • K
  • Kepper, Johannes
  • Kornstädt, Andreas
  • L
  • Lartillot, Olivier
  • M
  • Marsden, Alan
  • Mueller, Meinard
  • Müllensiefen, Daniel
  • Müller, Meinard
  • R
  • Roland, Perry
  • Röwenstruck, Daniel
  • S
  • Sapp, Craig
  • Selfridge-Field, Eleanor
  • W
  • Wiering, Frans
  • Wiggins, Geraint A.

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