Timbre Characterization with Mel-Cepstrum and Neural Nets (Contributo in atti di convegno)

Type
Label
  • Timbre Characterization with Mel-Cepstrum and Neural Nets (Contributo in atti di convegno) (literal)
Anno
  • 1994-01-01T00:00:00+01:00 (literal)
Alternative label
  • Cosi P., De Poli G., Prandoni P. (1994)
    Timbre Characterization with Mel-Cepstrum and Neural Nets
    in Proceedings ICMC-1994, International Computer Music Association - Psychoacoustics, Perception, 1994, San Francisco, 1994
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Cosi P., De Poli G., Prandoni P. (literal)
Pagina inizio
  • 42 (literal)
Pagina fine
  • 45 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Cosi P., De Poli G., Prandoni P. Timbre Characterization with Mel-Cepstrum and Neural Nets Proceedings ICMC-1994, Psychoacoustics, Perception, 1994 pp. 42-45 http://quod.lib.umich.edu/i/icmc/bbp2372.1994.012/--timbre-characterization-with-mel-cepstrum-and-neural-nets?view=image (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://quod.lib.umich.edu/i/icmc/bbp2372.1994.012/--timbre-characterization-with-mel-cepstrum-and-neural-nets?view=image (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proceedings of ICMC 1994 - International Computer Music Conference proceedings (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 1994 (literal)
Note
  • SeerX (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISTC CNR, UOS Padova Cosi P. University of Padova De Poli G., Prandoni P. (literal)
Titolo
  • Timbre Characterization with Mel-Cepstrum and Neural Nets (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • International Computer Music Association (literal)
Abstract
  • In this work the problem of timbre recognition--classification is addressed by com-bining the properties of a powerful speech-coding technique, the Mel-frequency Cepstral Coefficients, with the feature extraction capabilities of a self-organizing neural network. Acoustic relationships between tones are reflected into spatial relationships onto a neural lattice. Final results are in good agreement with the usual classifications of timbre quality, and offer promising grounds for the con¬struction of a general, analysis-based timbre space. (literal)
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