Nearest Centroid error Clustering for radial/elliptical Basis Function Neural Networks in Timbre Classification

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.