Elaborating Advanced Machine Learning Techniques in the Music Class

Abstract

In music education, there are several cases where the instructor needs to set preparatory tasks and use verbal communication, both of which, nonetheless, interrupt the music continuity. These “interruptions” are considered as learning barriers. Having researched teaching communication habits on several music instruction cases, we have come up with the idea of designing a set of software blocks that, laid down together as a digital aid to the class, can generously assist music teaching by providing communication facilitators in a wide range of commonly used music teaching exercise tasks. In this direction, a range of algorithms and software blocks have been implemented at the Ionian University using the Max/MSPTM dedicated software platform, comprising the FIG set of tools. A specific subset of these software tools has included Machine Learning (ML) logic in order to promote a wiser instructor-student communication that advances class musicality and potentially facilitates deeper consolidation of musical structures.