# Parametric Optimization of Violin Top Plates using Machine Learning

arXiv: https://arxiv.org/abs/2102.07133 · HTML: https://mlsanity.com/paper/2102.07133/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [A Data-Driven Approach to Violin Making](https://mlsanity.com/paper/2102.04254/) (2021)

## Built on by (most cited first, 1 of 1)

- [Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network](https://mlsanity.com/paper/2103.16935/) (2021)

## Abstract (arXiv)

We recently developed a neural network that receives as input the geometrical and mechanical parameters that define a violin top plate and gives as output its first ten eigenfrequencies computed in free boundary conditions. In this manuscript, we use the network to optimize several error functions, with the goal of analyzing the relationship between the eigenspectrum problem for violin top plates and their geometry. First, we focus on the violin outline. Given a vibratory feature, we find which is the best geometry of the plate to obtain it. Second, we investigate whether, from the vibrational point of view, a change in the outline shape can be compensated by one in the thickness distribution and vice versa. Finally, we analyze how to modify the violin shape to keep its response constant as its material properties vary. This is an original technique in musical acoustics, where artificial intelligence is not widely used yet. It allows us to both compute the vibrational behavior of an instrument from its geometry and optimize its shape for a given response. Furthermore, this method can be of great help to violin makers, who can thus easily understand the effects of the geometry changes in the violins they build, shedding light on one of the most relevant and, at the same time, less understood aspects of the construction process of musical instruments.