LNCS 2771 Musical Pattern Extraction Using
Genetic Algorithms 1st Edition by Carlos Grilo,
Amilcar Cardoso ISBN 3540209220 9783540209225
pdf download
https://ebookball.com/product/lncs-2771-musical-pattern-
extraction-using-genetic-algorithms-1st-edition-by-carlos-grilo-
amilcar-cardoso-isbn-3540209220-9783540209225-12188/
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LNCS 2771 The Study of Musical Scales in Central Africa The Use of
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isbn-3540209220-9783540209225-13142/
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Knipping ISBN 3540209220 9783540209225
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978-3540226758
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Robust Range Image Registration Using Genetic Algorithms And The
Surface Interpenetration Measure 1st edition by Kim L Boyer, Luciano
Silva, Olga R P Bellon ISBN ‎ 9812561080  978-9812561084
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, Musical Pattern Extraction Using Genetic Algorithms
Carlos Grilo1,2 and Amilcar Cardoso2
1 Departamento de Engenharia Informática da Escola Superior Tecnlogia e Gestã de Leria
Morro do Lena, Alto Vieiro, 2401-951- Leiria, Portugal
2 Centro de Informática e Sistemas da Universidade de Coimbra
Polo II, Pinhal de Marrocos, 3030 - Coimbra, Portugal
{grilo,amilcar}@dei.uc.pt
Abstract. This paper describes a research work in which we study the possibil-
ity of applying genetic algorithms to the extraction of musical patterns in mono-
phonic musical pieces. Each individual in the population represents a possible
segmentation of the piece being analysed. The goal is to find a segmentation that
allows the identification of the most significant patterns of the piece. In order to
calculate an individual’s fitness, all its segments are compared among each other.
The bigger the area occupied by similar segments the better the quality of the
segmentation.
1 Introduction
In artificial intelligence, it is common to name the process of identifying the most mean-
ingful patterns occurring in some piece of data as pattern extraction. An important
branch of this investigation area is dedicated to the problem of identifying the most
meaningful patterns in data that can be represented as strings of symbols. This is a
problem of great relevance in areas like molecular biology, finance or music. In the
particular case of music, the identification of these patterns is crucial for tasks like, for
example, analysis, interactive on-line composition or music retrieval [1].
In previous work done in this area ([2–4]) the musical piece to be analysed can
be almost exhaustively scanned, so that all the existing patterns are identified. After
that, some criteria are applied so that the most meaningful patterns can be identified.
While this may be an effective procedure, we think that it is reasonable to investigate
the possibility of identifying the most meaningful patterns existing in one musical piece
without searching the entire space of its segments.
This paper describes a research work in which we study the application of genetic
algorithms to the extraction of musical patterns in monophonic musical pieces. The two
main reasons for choosing genetic algorithms to this type of problems are: the search
capacity already demonstrated by these algorithms in very complex problems; the possi-
bility of representing individuals as possible segmentations of the piece being analysed.
This way, if we guide the search such that segmentations with the most meaningful pat-
terns are favoured, at the end it will not be necessary to do extra processing. Since until
now we have been more concerned with the question of “how can this be done?” and
with “does it solve the problem?” than with “how fast it is”, this paper will not cover
aspects related to time performance.
U.K. Wiil (Ed.): CMMR 2003, LNCS 2771, pp. 114–123, 2004.
c Springer-Verlag Berlin Heidelberg 2004
Genetic Algorithms 1st Edition by Carlos Grilo,
Amilcar Cardoso ISBN 3540209220 9783540209225
pdf download
https://ebookball.com/product/lncs-2771-musical-pattern-
extraction-using-genetic-algorithms-1st-edition-by-carlos-grilo-
amilcar-cardoso-isbn-3540209220-9783540209225-12188/
Explore and download more ebooks or textbooks
at ebookball.com
, Get Your Digital Files Instantly: PDF, ePub, MOBI and More
Quick Digital Downloads: PDF, ePub, MOBI and Other Formats
LNCS 2771 The Study of Musical Scales in Central Africa The Use of
Interactive Experimental Methods 1st Edition by Fabrice Marandola ISBN
3540209220 9783540209225
https://ebookball.com/product/lncs-2771-the-study-of-musical-
scales-in-central-africa-the-use-of-interactive-experimental-
methods-1st-edition-by-fabrice-marandola-
isbn-3540209220-9783540209225-10482/
LNCS 2771 Automatic Extraction of Approximate Repetitions in
Polyphonic Midi Files Based on Perceptive Criteria 1st Edition by
Benoit Meudic, Emmanuel James ISBN 3540209220 9783540209225
https://ebookball.com/product/lncs-2771-automatic-extraction-of-
approximate-repetitions-in-polyphonic-midi-files-based-on-
perceptive-criteria-1st-edition-by-benoit-meudic-emmanuel-james-
isbn-3540209220-9783540209225-13142/
LNCS 2771 Ambiguous Live Exploring Collaborative Dynamic Control of
MIDI Sequencers 1st Edition by David Eyers ISBN 3540209220
9783540209225
https://ebookball.com/product/lncs-2771-ambiguous-live-exploring-
collaborative-dynamic-control-of-midi-sequencers-1st-edition-by-
david-eyers-isbn-3540209220-9783540209225-11354/
LNCS 2771 Conserving an Ancient Art of Music Making SID Tunes
Editable 1st Edition by Gerald Friedland, Kristian Jantz, Lars
Knipping ISBN 3540209220 9783540209225
https://ebookball.com/product/lncs-2771-conserving-an-ancient-
art-of-music-making-sid-tunes-editable-1st-edition-by-gerald-
friedland-kristian-jantz-lars-knipping-
isbn-3540209220-9783540209225-9980/
,LNCS 3117 Extraction of Myocardial Contractility Patterns from Short
Axes MR Images Using Independent Component Analysis 1st edition by
Suinesiaputra, Frangi, Üzümcü, Reiber, Lelievedt ISBN 3540226753
978-3540226758
https://ebookball.com/product/lncs-3117-extraction-of-myocardial-
contractility-patterns-from-short-axes-mr-images-using-
independent-component-analysis-1st-edition-by-suinesiaputra-
frangi-aoeza1-4mca1-4-reiber-lelievedt-isbn-354022/
Robust Range Image Registration Using Genetic Algorithms And The
Surface Interpenetration Measure 1st edition by Kim L Boyer, Luciano
Silva, Olga R P Bellon ISBN ‎ 9812561080  978-9812561084
https://ebookball.com/product/robust-range-image-registration-
using-genetic-algorithms-and-the-surface-interpenetration-
measure-1st-edition-by-kim-l-boyer-luciano-silva-olga-r-p-bellon-
isbn-aeurz-9812561080-978-9812561084-19554/
Framework for Workflow Gridication of Genetic Algorithms in Java 1st
edtion by Boro Jakimovski, Darko Cerepnalkoski, Goran Velinov ISBN
3540693888 9783540693888
https://ebookball.com/product/framework-for-workflow-gridication-
of-genetic-algorithms-in-java-1st-edtion-by-boro-jakimovski-
darko-cerepnalkoski-goran-velinov-
isbn-3540693888-9783540693888-12822/
LNCS 3058 Typical Sequences Extraction and Recognition 1st edition by
Gengyu Ma, Xueyin Lin ISBN 3540220127 978-3540220121
https://ebookball.com/product/lncs-3058-typical-sequences-
extraction-and-recognition-1st-edition-by-gengyu-ma-xueyin-lin-
isbn-3540220127-978-3540220121-10046/
Pattern Recognition for Brain Computer Interfaces by Combining Support
Vector Machine with Adaptive Genetic Algorithm 1st Edition by Banghua
Yang, Shiwei Ma, Zhihua Li 9783540747703
https://ebookball.com/product/pattern-recognition-for-brain-
computer-interfaces-by-combining-support-vector-machine-with-
adaptive-genetic-algorithm-1st-edition-by-banghua-yang-shiwei-ma-
zhihua-li-9783540747703-13336/
, Musical Pattern Extraction Using Genetic Algorithms
Carlos Grilo1,2 and Amilcar Cardoso2
1 Departamento de Engenharia Informática da Escola Superior Tecnlogia e Gestã de Leria
Morro do Lena, Alto Vieiro, 2401-951- Leiria, Portugal
2 Centro de Informática e Sistemas da Universidade de Coimbra
Polo II, Pinhal de Marrocos, 3030 - Coimbra, Portugal
{grilo,amilcar}@dei.uc.pt
Abstract. This paper describes a research work in which we study the possibil-
ity of applying genetic algorithms to the extraction of musical patterns in mono-
phonic musical pieces. Each individual in the population represents a possible
segmentation of the piece being analysed. The goal is to find a segmentation that
allows the identification of the most significant patterns of the piece. In order to
calculate an individual’s fitness, all its segments are compared among each other.
The bigger the area occupied by similar segments the better the quality of the
segmentation.
1 Introduction
In artificial intelligence, it is common to name the process of identifying the most mean-
ingful patterns occurring in some piece of data as pattern extraction. An important
branch of this investigation area is dedicated to the problem of identifying the most
meaningful patterns in data that can be represented as strings of symbols. This is a
problem of great relevance in areas like molecular biology, finance or music. In the
particular case of music, the identification of these patterns is crucial for tasks like, for
example, analysis, interactive on-line composition or music retrieval [1].
In previous work done in this area ([2–4]) the musical piece to be analysed can
be almost exhaustively scanned, so that all the existing patterns are identified. After
that, some criteria are applied so that the most meaningful patterns can be identified.
While this may be an effective procedure, we think that it is reasonable to investigate
the possibility of identifying the most meaningful patterns existing in one musical piece
without searching the entire space of its segments.
This paper describes a research work in which we study the application of genetic
algorithms to the extraction of musical patterns in monophonic musical pieces. The two
main reasons for choosing genetic algorithms to this type of problems are: the search
capacity already demonstrated by these algorithms in very complex problems; the possi-
bility of representing individuals as possible segmentations of the piece being analysed.
This way, if we guide the search such that segmentations with the most meaningful pat-
terns are favoured, at the end it will not be necessary to do extra processing. Since until
now we have been more concerned with the question of “how can this be done?” and
with “does it solve the problem?” than with “how fast it is”, this paper will not cover
aspects related to time performance.
U.K. Wiil (Ed.): CMMR 2003, LNCS 2771, pp. 114–123, 2004.
c Springer-Verlag Berlin Heidelberg 2004
