skip to main content
Idioma:

10 - Intelligent Music Composition

Kaliakatsos-Papakostas, Maximos A. ; Floros, Andreas ; Vrahatis, Michael N.

Swarm Intelligence and Bio-Inspired Computation, 2013, p.239-256

Elsevier Inc

Texto completo disponível

Citações Citado por
  • Título:
    10 - Intelligent Music Composition
  • Autor: Kaliakatsos-Papakostas, Maximos A. ; Floros, Andreas ; Vrahatis, Michael N.
  • Assuntos: automatic music composition ; intelligent algorithms ; Intelligent music composition ; interactive music composition ; supervised music composition ; unsupervised music composition
  • É parte de: Swarm Intelligence and Bio-Inspired Computation, 2013, p.239-256
  • Descrição: Automatic music composition has blossomed with the introduction of intelligent methodologies in computer science. Thereby, many methodologies for automatic music composition have been or could be described as “intelligent,” but what exactly is it that makes them intelligent? Furthermore, is there any categorization of intelligent music composition (IMC) methodologies that is both consistent and descriptive? This chapter aims to provide some insights on what IMC methodologies are, through proposing and analyzing a detailed categorization of them. Toward this perspective, methodologies that incorporate bioinspired intelligent algorithms (such as cellular automata, L-systems, genetic algorithms, swarm intelligence, among others) as well as their combinations are considered and briefly reviewed. At the same time, a consistent categorization of these methodologies is proposed, taking into account the utilization of their intelligent algorithm in accordance to their overall compositional aims. To this end, three main categories can be defined: the “unsupervised,” the “supervised,” and the “interactive” IMC methodologies.
  • Editor: Elsevier Inc
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.