Stylistic Classification of Historical Violins: A Deep Learning Approach

Stylistic study of artworks is a well-known problem in the Cultural Heritage field. Traditional artworks, such as statues and paintings, have been extensively studied by art experts, producing standard methodologies to analyze and recognize the style of an artist. In this context, the case of historical violins is peculiar. Even if the main stylistic features of a violin are known, only few experts are capable to attribute a violin to its maker with a high degree of certainty. This paper presents a study about the use of deep learning to discriminate a violin style. Firstly, we collected images of 17th–18th century violins held, or in temporary loan, at “Museo del Violino” of Cremona (Italy) to be used as reference dataset. Then, we tested the performances of three state-of-the-art CNNs (VGG16, ResNet50 and InceptionV3) on a binary classification (Stradivari vs. NotStradivari). The best performing model was able to achieve 77.27% accuracy and 0.72 F1 score. A promising result, keeping in mind the limited amount of data and the complexity of the task, even for human experts. Finally, we compared the regions of interest identified by the network with the regions of interest identified in a previous eye tracking study conducted on expert luthiers, to highlight similarity and differences between the two behaviors.