Nearest Centroid Error Clustering for radial/elliptical basis function neural networks in timbre classification

Tae Hong Park, Perry Cook

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a neural network approach for classification of musical instrument sounds through Radial and Elliptical Basic Functions. In particular, we discuss a novel automatic network fine-tuning method called Nearest Centroid Error Clustering (NCC) which determines a robust number of centroids for improved system performance. 829 monophonic sound examples from the string, brass, and woodwind families were used. A number of different performance techniques, dynamics, and pitches were utilized in training and testing the system resulting in 71% correct individual instrument classification (12 classes) and 88% correct instrument family (3 classes) classification.

Original languageEnglish (US)
Title of host publicationInternational Computer Music Conference, ICMC 2005
PublisherInternational Computer Music Association
StatePublished - 2005
EventInternational Computer Music Conference, ICMC 2005 - Barcelona, Spain
Duration: Sep 5 2005Sep 9 2005

Other

OtherInternational Computer Music Conference, ICMC 2005
CountrySpain
CityBarcelona
Period9/5/059/9/05

ASJC Scopus subject areas

  • Computer Science Applications
  • Media Technology
  • Music

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