In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining

Olivier Lartillot

In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining

Olivier Lartillot

Department of Architecture, Design and Media Technology

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

7 Citations (Scopus)

259 Downloads (Pure)

Abstract

The paper describes a computational system for exhaustive but compact description of repeated motivic patterns in symbolic representations of music. The approach follows a method based on closed heterogeneous pattern mining in multiparametrical space with control of pattern cyclicity. This paper presents a much simpler description and justification of this general strategy, as well as significant simplifications of the model, in particular concerning the management of pattern cyclicity.
A new method for automated bundling of patterns belonging to same motivic or thematic classes is also presented.

The good performance of the method is shown through the analysis of a piece from the JKUPDD database. Ground-truth motives are detected, while additional relevant information completes the ground-truth musicological analysis.

The system, implemented in Matlab, is made publicly available as part of MiningSuite, a new open-source framework for audio and music analysis.

Original language	English
Title of host publication	15th International Society for Music Information Retrieval Conference
Editors	Hsin-Min Wang, Yi-Hsuan Yang, Jin Ha Lee
Number of pages	6
Publication date	2014
Publication status	Published - 2014
Event	International Symposium on Music Information Retrieval: ISMIR - Taipei, Taiwan, Province of China Duration: 27 Oct 2014 → 31 Oct 2014

Conference

Conference	International Symposium on Music Information Retrieval
Country/Territory	Taiwan, Province of China
City	Taipei
Period	27/10/2014 → 31/10/2014

Keywords

Motivic analysis
Pattern discovery

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LartillotAccepted author manuscript, 421 KB

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Lrn2Cre8: Learning to Create
Meredith, D. & Bemman, B.
EU Seventh Framework Programme (FP7)
01/10/2013 → 30/09/2016
Project: Research

Cite this

@inproceedings{7d107f272fd74343a0aec03fe29ef6c2,

title = "In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining",

abstract = "The paper describes a computational system for exhaustive but compact description of repeated motivic patterns in symbolic representations of music. The approach follows a method based on closed heterogeneous pattern mining in multiparametrical space with control of pattern cyclicity. This paper presents a much simpler description and justification of this general strategy, as well as significant simplifications of the model, in particular concerning the management of pattern cyclicity.A new method for automated bundling of patterns belonging to same motivic or thematic classes is also presented.The good performance of the method is shown through the analysis of a piece from the JKUPDD database. Ground-truth motives are detected, while additional relevant information completes the ground-truth musicological analysis.The system, implemented in Matlab, is made publicly available as part of MiningSuite, a new open-source framework for audio and music analysis.",

keywords = "Motivic analysis, Pattern discovery",

author = "Olivier Lartillot",

year = "2014",

language = "English",

editor = "Hsin-Min Wang and Yi-Hsuan Yang and Lee, {Jin Ha}",

booktitle = "15th International Society for Music Information Retrieval Conference",

note = "International Symposium on Music Information Retrieval : ISMIR ; Conference date: 27-10-2014 Through 31-10-2014",

}

Lartillot, O 2014, In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining. in H-M Wang, Y-H Yang & JH Lee (eds), 15th International Society for Music Information Retrieval Conference. International Symposium on Music Information Retrieval, Taipei, Taiwan, Province of China, 27/10/2014. <http://www.terasoft.com.tw/conf/ismir2014/ConferenceProgram.html#a3>

In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining. / Lartillot, Olivier.
15th International Society for Music Information Retrieval Conference. ed. / Hsin-Min Wang; Yi-Hsuan Yang; Jin Ha Lee. 2014.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining

AU - Lartillot, Olivier

PY - 2014

Y1 - 2014

N2 - The paper describes a computational system for exhaustive but compact description of repeated motivic patterns in symbolic representations of music. The approach follows a method based on closed heterogeneous pattern mining in multiparametrical space with control of pattern cyclicity. This paper presents a much simpler description and justification of this general strategy, as well as significant simplifications of the model, in particular concerning the management of pattern cyclicity.A new method for automated bundling of patterns belonging to same motivic or thematic classes is also presented.The good performance of the method is shown through the analysis of a piece from the JKUPDD database. Ground-truth motives are detected, while additional relevant information completes the ground-truth musicological analysis.The system, implemented in Matlab, is made publicly available as part of MiningSuite, a new open-source framework for audio and music analysis.

AB - The paper describes a computational system for exhaustive but compact description of repeated motivic patterns in symbolic representations of music. The approach follows a method based on closed heterogeneous pattern mining in multiparametrical space with control of pattern cyclicity. This paper presents a much simpler description and justification of this general strategy, as well as significant simplifications of the model, in particular concerning the management of pattern cyclicity.A new method for automated bundling of patterns belonging to same motivic or thematic classes is also presented.The good performance of the method is shown through the analysis of a piece from the JKUPDD database. Ground-truth motives are detected, while additional relevant information completes the ground-truth musicological analysis.The system, implemented in Matlab, is made publicly available as part of MiningSuite, a new open-source framework for audio and music analysis.

KW - Motivic analysis

KW - Pattern discovery

M3 - Article in proceeding

BT - 15th International Society for Music Information Retrieval Conference

A2 - Wang, Hsin-Min

A2 - Yang, Yi-Hsuan

A2 - Lee, Jin Ha

T2 - International Symposium on Music Information Retrieval

Y2 - 27 October 2014 through 31 October 2014

ER -

In-depth motivic analysis based on multiparametric closed pattern and cyclic sequence mining

Abstract

Conference

Keywords

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AUB Link

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Lrn2Cre8: Learning to Create

Cite this