Using Heuristic Optimization for Segmentation of Symbolic Music

Brigitte Rafael; Stefan Örtl; Michael Affenzeller; Stefan Wagner

Abstract

Solving the segmentation problem for music is a key issue in music information retrieval (MIR). Structural information about a composition achieved by music segmentation can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. Various approaches using genetic algorithms have already been introduced to the field of media segmentation including image and video segmentation as segmentation problems usually have complex fitness landscapes. The authors of this paper present an approach to apply genetic algorithms to the music segmentation problem.

Original language	English
Pages	220-221
Publication status	Published - 2009
Event	Twelve International Conference on Computer Aided Systems Theory 2009 - Las Palmas, Spain Duration: 15 Feb 2009 → 20 Feb 2009 http://www.iuctc.ulpgc.es/spain/eurocast2009/index.html

Conference

Conference	Twelve International Conference on Computer Aided Systems Theory 2009
Country/Territory	Spain
City	Las Palmas
Period	15.02.2009 → 20.02.2009
Internet address	http://www.iuctc.ulpgc.es/spain/eurocast2009/index.html

Cite this

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title = "Using Heuristic Optimization for Segmentation of Symbolic Music",

abstract = "Solving the segmentation problem for music is a key issue in music information retrieval (MIR). Structural information about a composition achieved by music segmentation can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. Various approaches using genetic algorithms have already been introduced to the field of media segmentation including image and video segmentation as segmentation problems usually have complex fitness landscapes. The authors of this paper present an approach to apply genetic algorithms to the music segmentation problem.",

author = "Brigitte Rafael and Stefan {\"O}rtl and Michael Affenzeller and Stefan Wagner",

year = "2009",

language = "English",

pages = "220--221",

note = "Twelve International Conference on Computer Aided Systems Theory 2009 ; Conference date: 15-02-2009 Through 20-02-2009",

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TY - CONF

T1 - Using Heuristic Optimization for Segmentation of Symbolic Music

AU - Rafael, Brigitte

AU - Örtl, Stefan

AU - Affenzeller, Michael

AU - Wagner, Stefan

PY - 2009

Y1 - 2009

N2 - Solving the segmentation problem for music is a key issue in music information retrieval (MIR). Structural information about a composition achieved by music segmentation can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. Various approaches using genetic algorithms have already been introduced to the field of media segmentation including image and video segmentation as segmentation problems usually have complex fitness landscapes. The authors of this paper present an approach to apply genetic algorithms to the music segmentation problem.

AB - Solving the segmentation problem for music is a key issue in music information retrieval (MIR). Structural information about a composition achieved by music segmentation can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. Various approaches using genetic algorithms have already been introduced to the field of media segmentation including image and video segmentation as segmentation problems usually have complex fitness landscapes. The authors of this paper present an approach to apply genetic algorithms to the music segmentation problem.

M3 - Abstract

SP - 220

EP - 221

T2 - Twelve International Conference on Computer Aided Systems Theory 2009

Y2 - 15 February 2009 through 20 February 2009

ER -

Using Heuristic Optimization for Segmentation of Symbolic Music

Abstract

Conference

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