This paper proposes a preprocessing technique for the automatic transcription of performances produced by a musical instrument (or other sound source) capable of timbre variations. Voice recognition techniques will be exploited to gather information about timbre, then a clustering approach will be used to reduce data cardinality, and, finally, data dimensionality will be further reduced using multi-dimensional scaling to create labels as points in a data-driven timbre-space. A graphical visualization of the achieved results will be implemented in order to verify the achievement of the initial requirements. A MATLAB toolkit performing the operations described in this paper is publicly available to test the effectiveness of the proposed approach.
Automatic Annotation of Timbre Variation for Musical Instruments / G. Haus, L.A. Ludovico, G. Presti - In: Computer Music Multidisciplinary Research / [a cura di] R. Kronland-Martinet, S. Ystad, M. Aramaki. - [s.l] : Les éditions de PRISM, 2017 Aug. - ISBN 9791097498009. - pp. 493-504 (( Intervento presentato al 13. convegno Computer Music Multidisciplinary Research tenutosi a Matosinhos nel 2017.
Automatic Annotation of Timbre Variation for Musical Instruments
G. Haus;L.A. Ludovico;G. Presti
2017
Abstract
This paper proposes a preprocessing technique for the automatic transcription of performances produced by a musical instrument (or other sound source) capable of timbre variations. Voice recognition techniques will be exploited to gather information about timbre, then a clustering approach will be used to reduce data cardinality, and, finally, data dimensionality will be further reduced using multi-dimensional scaling to create labels as points in a data-driven timbre-space. A graphical visualization of the achieved results will be implemented in order to verify the achievement of the initial requirements. A MATLAB toolkit performing the operations described in this paper is publicly available to test the effectiveness of the proposed approach.File | Dimensione | Formato | |
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