Radial/Elliptical basis function neural networks for timbre classification

Tae Hong Park (1) , Perry Cook (2)

1 Department of Music [Tulane]
2 Princeton University

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.

Domains: Computer Science [cs]; Musicology and performing arts

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⟩