Music compression system using the GA

Hiroshi Kawasaki, Yasue Mitsukura, Kensuke Mitsukura, Minoru Fukumi, Norio Akamatsu

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a new system identification method by using a genetic algorithm (GA) which has a hybrid structure. The hybrid structure means that a GA has 2 structures. One is the most popular chromosome type GA. That is, chromosomes have binary type genes. The other one is real coded GA. The former is used for determining a function type automatically. The latter is used for determining the coefficient of the function, time delay in the system and combination of the functions automatically. Finally, in order to show the effectiveness of the proposed method, computer simulations were done. Furthermore, in the computer simulations, 2-kinds of systems are identified. One is the hammer stain model. The other is a complex model. From these simulation results, the effectiveness of the proposed method is cleared.

Original languageEnglish
Pages (from-to)827-832
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3213
Publication statusPublished - 2004
Externally publishedYes

Fingerprint

Music
Compression
Genetic algorithms
Genetic Algorithm
Chromosome
Computer Simulation
Chromosomes
Real-coded Genetic Algorithm
System Identification
Hammers
Time Delay
Computer simulation
Binary
Gene
Time delay
Identification (control systems)
Coloring Agents
Genes
Coefficient
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Music compression system using the GA. / Kawasaki, Hiroshi; Mitsukura, Yasue; Mitsukura, Kensuke; Fukumi, Minoru; Akamatsu, Norio.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3213, 2004, p. 827-832.

Research output: Contribution to journalArticle

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