A data-driven approach to violin making

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.

Automated summary

The article demonstrates the prediction of modal frequencies of violin tops from geometric parameters using standard statistical learning tools, and the successful application of artificial intelligence in traditional violin making. It also discusses the variation of modal frequencies with plate thickness, known as plate tuning, and proposes a predictive tool for plate tuning that considers material and geometric parameters.