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Dynamic Neural Networks Applied to Melody Retrieval

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Advances in Soft Computing (MICAI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6438))

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Abstract

A new method for the retrieval of melodies from a database is described in this paper. For its functioning, the method makes use of Dynamic Neural Networks (DNN). During training a set ofDNN is first trained with information of the melodies to be retrieved. Instead of using traditional signal descriptors we use the matrix of synaptic weights that can be efficiently used for melody representation and retrieval. Most of the reported works have been focused on the symbolic representation of musical information. None of them have provided good results with original signals.

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Author information

Authors and Affiliations

  1. Centro de Investigación en Computación-IPN, Unidad Profesional Adolfo-López Mateos, Av. Juan de Dios Bátiz s/n and M. Othón de Mendizábal, Zacatenco, México, DF., 07738, Mexico

    Laura E. Gomez, Humberto Sossa, Ricardo Barron & Julio F. Jimenez

Authors
  1. Laura E. Gomez
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  2. Humberto Sossa
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  3. Ricardo Barron
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  4. Julio F. Jimenez
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, , Av. Juan Dios Batiz, s/n, Zacatenco, 07738, Mexico City, México

    Grigori Sidorov

  2. Centro de Investigación en Matemáticas (CIMAT), Area de Computación, Callejón de Jalisco s/n, Mineral de Valenciana, Guanajuato, 36240, Guanajuato, México

    Arturo Hernández Aguirre

  3. Ciencias Computacionales, Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE), Luis Enrique Erro No. 1, Santa María Tonantzintla, 72840, Puebla, México

    Carlos Alberto Reyes García

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Gomez, L.E., Sossa, H., Barron, R., Jimenez, J.F. (2010). Dynamic Neural Networks Applied to Melody Retrieval. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_23

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  • DOI: https://doi.org/10.1007/978-3-642-16773-7_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16772-0

  • Online ISBN: 978-3-642-16773-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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