The objective ear: a tool for assessing music education progress

Abstract

The objective ear is an application that, given a pair of performances of a piece of music, judges the amount of progress made between the two performances. The application has two components: an evaluator and a classifier. The evaluator component analyzes each performance to generate a vector of metrics. These vectors are subtracted from each other to give a vector of differences. The difference vector is used as input to a decision tree, a machine learning classifier, which assigns a level of progress to the pair of performances. Testing of the classifier shows that the application provides accurate assessments and could be used in music education environments to aid students in assessing their progress, and to provide useful data on how music students progress.