A Data-Driven Approach to Violin Making

TL;DR

This paper demonstrates that statistical learning and AI can predict violin top modal frequencies from geometric parameters, advancing scientific understanding and providing a new tool for traditional violin making.

Contribution

It introduces a data-driven method to predict violin vibrational properties from shape and thickness, bridging tradition and scientific analysis.

Findings

Modal frequencies can be predicted from geometric parameters.

AI effectively models the relationship between shape and vibrational properties.

A predictive tool for plate tuning considering material and geometry is proposed.

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 {\em 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.