Radial/Elliptical basis function neural networks for timbre classification - Archive ouverte HAL Access content directly
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Radial/Elliptical basis function neural networks for timbre classification

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Abstract

This paper outlines a RBF/EBF neural network approach for automatic musical instrument classification using salient feature extraction techniques with a combination of supervised and unsupervised learning schemes. 829 monophonic sound examples (86% Siedlaczek Library [2], 14% other sources) from the string, brass, and woodwind families with a variety of performance techniques, dynamics, and pitches were used for the development of feature extraction, network initialization algorithms, and training of the neural networks resulting in approximately 71% individual instrument and 88% instrument family classification. A novel approach for automatically fine-tuning the system using the Nearest Centroid Error Clustering (NCC) method which determines a robust number of centroids is also discussed.
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Dates and versions

hal-03114906 , version 1 (19-01-2021)

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  • HAL Id : hal-03114906 , version 1

Cite

Tae Hong Park, Perry Cook. Radial/Elliptical basis function neural networks for timbre classification. Journées d'Informatique Musicale 2005, Association Française d'Informatique Musicale; Centre de recherche en Informatique et Création Musicale, Jun 2005, Saint-Denis, France. ⟨hal-03114906⟩
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