Inducing jury's preferences in terms of acoustic features of violin sounds

Abstract

A set of violins submitted to a competition has been evaluated by the jury from the viewpoint of several criteria and then ranked from the best to the worst. The sound of the instruments played by violinists during the competition has been recorded digitally and then processed to obtain sound attributes. Given the jury's ranking of violins according to sound quality criteria, we are inferring from the sound characteristics a preference model of the jury in the form of "if..., then..." decision rules. This preference model explains the given ranking and permits to build a ranking of a new set of violins according to this policy. The inference follows the scheme of an inductive supervised learning. For this, we are applying a special computational tool called Dominance-based Rough Set Approach (DESA). The new set of attributes derived from the energy of consecutive halftones of the chromatic scales played on four strings has proved a good accuracy of the approximation.