In order to map the spectral characteristics of the great variety of sounds a musical instrument may produce, different notes were performed and sampled in several intensity levels across the whole extension of a clarinet. Amplitude and frequency time-varying curves of partials were measured by Discrete Fourier Transform. A limited set of orthogonal spectral bases was derived by Principal Component Analysis techniques. These bases defined spectral sub-spaces capable of representing all tested sounds, which were validated by auditory tests. Sub-spaces involving larger groups of notes were used to compare the sounds according to the distance metrics of the representation. A clustering algorithm was used to infer timbre classes. Preliminary tests with resynthesized sounds with normalized pitch showed a clear relation between the perceived timbre and the cluster label to which the notes were assigned.
Cite as: Paula, H.d., Yehia, H., Loureiro, M.A. (2004) Representation and classification of the timbre space of a single musical instrument. Proc. ITRW on Statistical and Perceptual Audio Processing (SAPA 2004), paper 86
@inproceedings{paula04_sapa,
author={Hugo de Paula and Hani Yehia and Mauricio A. Loureiro},
title={{Representation and classification of the timbre space of a single musical instrument}},
year=2004,
booktitle={Proc. ITRW on Statistical and Perceptual Audio Processing (SAPA 2004)},
pages={paper 86}
}