A data-driven approach to violin making

Abstract Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the s...

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Autores principales:	Sebastian Gonzalez, Davide Salvi, Daniel Baeza, Fabio Antonacci, Augusto Sarti
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/10ddbaa77f674ae2967e6b758a197029
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spelling	oai:doaj.org-article:10ddbaa77f674ae2967e6b758a1970292021-12-02T14:49:34ZA data-driven approach to violin making10.1038/s41598-021-88931-z2045-2322https://doaj.org/article/10ddbaa77f674ae2967e6b758a1970292021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88931-zhttps://doaj.org/toc/2045-2322Abstract Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.Sebastian GonzalezDavide SalviDaniel BaezaFabio AntonacciAugusto SartiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sebastian Gonzalez Davide Salvi Daniel Baeza Fabio Antonacci Augusto Sarti A data-driven approach to violin making
description	Abstract Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.
format	article
author	Sebastian Gonzalez Davide Salvi Daniel Baeza Fabio Antonacci Augusto Sarti
author_facet	Sebastian Gonzalez Davide Salvi Daniel Baeza Fabio Antonacci Augusto Sarti
author_sort	Sebastian Gonzalez
title	A data-driven approach to violin making
title_short	A data-driven approach to violin making
title_full	A data-driven approach to violin making
title_fullStr	A data-driven approach to violin making
title_full_unstemmed	A data-driven approach to violin making
title_sort	data-driven approach to violin making
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/10ddbaa77f674ae2967e6b758a197029
work_keys_str_mv	AT sebastiangonzalez adatadrivenapproachtoviolinmaking AT davidesalvi adatadrivenapproachtoviolinmaking AT danielbaeza adatadrivenapproachtoviolinmaking AT fabioantonacci adatadrivenapproachtoviolinmaking AT augustosarti adatadrivenapproachtoviolinmaking AT sebastiangonzalez datadrivenapproachtoviolinmaking AT davidesalvi datadrivenapproachtoviolinmaking AT danielbaeza datadrivenapproachtoviolinmaking AT fabioantonacci datadrivenapproachtoviolinmaking AT augustosarti datadrivenapproachtoviolinmaking
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A data-driven approach to violin making

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