OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

We propose a genetic algorithm (GA), OPTISIA, for efficiently finding optimal parameter combinations when running OMNISIA, a program that implements a family of analysis and compression algorithms based on the SIA point-set pattern discovery algorithm. The GA, when given a point-set representation of a piece of music as input, runs OMNISIA multiple times, attempting to evolve a combination of parameter values that achieves the highest compression factor on the input piece. When evaluated on two musicological tasks, the system consistently selected well-performing parameters for Forth’s algorithm compared to combinations found in published evaluations on the same musicological tasks.
OriginalsprogEngelsk
Titel12th International Workshop on Machine Learning and Music
Antal sider6
ForlagSpringer
StatusAccepteret/In press - 2020
BegivenhedInternational Workshop on Machine Learning and Music - Würzburg, Tyskland
Varighed: 16 sep. 201916 sep. 2019
Konferencens nummer: 12
https://musml2019.weebly.com/

Konference

KonferenceInternational Workshop on Machine Learning and Music
Nummer12
LandTyskland
ByWürzburg
Periode16/09/201916/09/2019
Internetadresse

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Set theory
Genetic algorithms

Citer dette

Schmuck, V., & Meredith, D. (Accepteret/In press). OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms. I 12th International Workshop on Machine Learning and Music Springer.
@inproceedings{417409630a3b471d901795efe3b62e87,
title = "OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms",
abstract = "We propose a genetic algorithm (GA), OPTISIA, for efficiently finding optimal parameter combinations when running OMNISIA, a program that implements a family of analysis and compression algorithms based on the SIA point-set pattern discovery algorithm. The GA, when given a point-set representation of a piece of music as input, runs OMNISIA multiple times, attempting to evolve a combination of parameter values that achieves the highest compression factor on the input piece. When evaluated on two musicological tasks, the system consistently selected well-performing parameters for Forth’s algorithm compared to combinations found in published evaluations on the same musicological tasks.",
author = "Viktor Schmuck and David Meredith",
year = "2020",
language = "English",
booktitle = "12th International Workshop on Machine Learning and Music",
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address = "Germany",

}

Schmuck, V & Meredith, D 2020, OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms. i 12th International Workshop on Machine Learning and Music. Springer, International Workshop on Machine Learning and Music, Würzburg, Tyskland, 16/09/2019.

OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms. / Schmuck, Viktor; Meredith, David.

12th International Workshop on Machine Learning and Music. Springer, 2020.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms

AU - Schmuck, Viktor

AU - Meredith, David

PY - 2020

Y1 - 2020

N2 - We propose a genetic algorithm (GA), OPTISIA, for efficiently finding optimal parameter combinations when running OMNISIA, a program that implements a family of analysis and compression algorithms based on the SIA point-set pattern discovery algorithm. The GA, when given a point-set representation of a piece of music as input, runs OMNISIA multiple times, attempting to evolve a combination of parameter values that achieves the highest compression factor on the input piece. When evaluated on two musicological tasks, the system consistently selected well-performing parameters for Forth’s algorithm compared to combinations found in published evaluations on the same musicological tasks.

AB - We propose a genetic algorithm (GA), OPTISIA, for efficiently finding optimal parameter combinations when running OMNISIA, a program that implements a family of analysis and compression algorithms based on the SIA point-set pattern discovery algorithm. The GA, when given a point-set representation of a piece of music as input, runs OMNISIA multiple times, attempting to evolve a combination of parameter values that achieves the highest compression factor on the input piece. When evaluated on two musicological tasks, the system consistently selected well-performing parameters for Forth’s algorithm compared to combinations found in published evaluations on the same musicological tasks.

M3 - Article in proceeding

BT - 12th International Workshop on Machine Learning and Music

PB - Springer

ER -