Abstract
The quantitative assessment of the musical timbre in an audio record is still an open-ended issue. Evaluating the musical timbre allows not only to establish precise musical parameters but also the recognition, classification of musical instruments, and assessment of the musical quality of a sound record. In this paper, we present a minimum set of dimensionless descriptors, motivated by musical acoustics, using the spectra obtained by the Fast Fourier Transform (FFT), which allows describing the timbre of wooden aerophones (Bassoon, Clarinet, Transverse Flute, and Oboe) using individual sound recordings of the musical tempered scale. We postulate that the proposed descriptors are sufficient to describe the timbral characteristics in the aerophones studied, allowing their recognition using the acoustic spectral signature. We believe that this approach can be further extended to use multidimensional unsupervised machine learning techniques, such as clustering, to obtain new insights into timbre characterization.
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