Abstract
This paper addresses the description of musical note attacks considering the influence of the reverberation. It is well known that attacks have an essential role in music performance. By manipulating note attack quality, musicians are able to control timbre, articulation, and rhythm, which are essential parameters for conveying their expressive intentions. Including information about the interaction with room acoustics enriches the study of musical performances in everyday practice conditions where reverberant environments are always present. Spectral Modeling decomposition was applied to evaluate independently, three components along the attack: (i) the harmonics of the note being played, (ii) the harmonics of the reverberation, (iii) the residue energy. The description proposal covers two stages: A 2D confrontation of the energy from the extracted components, and a profile representing the first nine harmonics’ structure. We tested the approach in a case study using recordings of an excerpt from a clarinet piece from the traditional classical repertoire, played by six professional musicians. MANOVA tests indicated significant differences (p < 0.05) when considering the musician as a factor for the 2D confrontation. Linear Discriminant Analysis applied for supervised dimensionality reduction of the harmonic profile data also indicated group separation to the same factor. We examined different legato, as well as articulated note transition presenting different performance technique demands.
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Notes
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The Pillai’s trace is the default test option used in R function MANOVA and considered the most robust statistic for general use, here chosen due to the exploratory characteristics of the research.
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Acknowledgments
This work was supported by CNPq (Brazilian National Council for Scientific and Technological Development).
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Campolina, T.d.A.M., Loureiro, M.A. (2021). Musical Note Attacks Description in Reverberant Environments. In: Kronland-Martinet, R., Ystad, S., Aramaki, M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science(), vol 12631. Springer, Cham. https://doi.org/10.1007/978-3-030-70210-6_16
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