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.

中文翻译：

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一种数据驱动的小提琴制作方法

在小提琴的所有特征中，与形状有关的特征可能是最重要的，因为小提琴制造商可以完全控制它们。然而，当代小提琴的制造仍然更多地基于传统而不是理解，并且关于形状和振动特性之间存在的特定关系的确定科学研究还没有到来，人们对此深感遗憾。在本文中，我们使用标准的统计学习工具显示，小提琴顶部的模态频率实际上可以从几何参数进行预测，并且人工智能可以成功地应用于传统的小提琴制作中。我们还研究了模态频率如何随板的厚度变化（这一过程通常称为{\ em板调整}），并讨论了这种依赖性的复杂性。最后，