# A Data-Driven Approach to Violin Making

arXiv: https://arxiv.org/abs/2102.04254 · HTML: https://mlsanity.com/paper/2102.04254/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Discovering physical concepts with neural networks](https://mlsanity.com/paper/1807.10300/) (2018)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Broadband DOA estimation using Convolutional neural networks trained with noise signals](https://mlsanity.com/paper/1705.00919/) (2017)

## Built on by (most cited first, 2 of 2)

- [Parametric Optimization of Violin Top Plates using Machine Learning](https://mlsanity.com/paper/2102.07133/) (2021)
- [Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network](https://mlsanity.com/paper/2103.16935/) (2021)

## Abstract (arXiv)

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.