Pattern Recognition Approach to Violin Shapes of MIMO database

TL;DR

This study uses statistical and pattern recognition methods to analyze violin shape evolution from museum data, revealing stability over time and relationships among makers and families.

Contribution

It introduces a novel approach combining PCA and thin plate splines to characterize and visualize violin shape variations and historical relationships.

Findings

Violin shapes are largely stable over centuries.

PCA uncovers maker and family similarities.

Shape analysis reveals no dominant design trend.

Abstract

Since the landmarks established by the Cremonese school in the 16th century, the history of violin design has been marked by experimentation. While great effort has been invested since the early 19th century by the scientific community on researching violin acoustics, substantially less attention has been given to the statistical characterization of how the violin shape evolved over time. In this paper we study the morphology of violins retrieved from the Musical Instrument Museums Online (MIMO) database -- the largest freely accessible platform providing information about instruments held in public museums. From the violin images, we derive a set of measurements that reflect relevant geometrical features of the instruments. The application of Principal Component Analysis (PCA) uncovered similarities between violin makers and their respective copyists, as well as among luthiers belonging to the same family lineage, in the context of historical narrative. Combined with a time-windowed approach, thin plate splines visualizations revealed that the average violin outline has remained mostly stable over time, not adhering to any particular trends of design across different periods in music history.