Collaborators for Machine Learning Aspects

Development of music education software inevitably leads to questions of how to acquire musical knowledge to be made available to the student user. I will describe machine learning of patterns for accompaniment styles and grammars for improvisation, based on melodic abstraction, clustering, and chaining. I will also discuss supervised and unsupervised approaches to improvising over chord progressions using neural network. Finally, I will mention a challenging unsolved application: learning to classify idiomatic patterns in chord progressions