This chapter pertains to the research in the field of music and artificial neural networks. The first attempts to codify human musical cognition through artificial neural networks are taken into account as well as recent and more complex techniques that allow computers to learn and recognize musical styles, genres, or even to compose music. Special topics covered are related to the representation of musical language and to the different systems used for solving them, from classic backpropagation networks to self-organizing maps and modular networks. The author hopes that this chapter will disclose some significant information about this emerging but nonetheless important subfield of AI and at the same time increase some interest and allow for a better understanding of this complex field.
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Ana B. Porto, Alejandro Pazos
Eduardo D. Martin, Alfonso Araque
Paulo Cortez, Miguel Rocha, José Neves
Julián Dorado, Nieves Pedreira, Mónica Miguelez
Daniel Manrique, Juan Rios, Alfonso Rodriguez-Paton
Daniel Rivero, Miguel Varela, Javier Pereira
Marcos G. Pose, Alberto C. Carollo, José M.A. Garda, Mari P. Gomez-Carracedo
Juan R. Rabunal, Juan Puertas
Belén Gonzalez, M. Isabel Martinez, Diego Carro
Robert Perkins, Anthony Brabazon
Alfonso Iglesias, Bernardino Arcay, José M. Cotos
Kun-Chang Lee, Tae-Young Paik
Tarun Bhaskar, Narasimha Kamath B.
Alejandra Rodriguez, Carlos Dafonte, Bernardino Arcay, Iciar Carricajo, Minia Manteiga