Real-time jam-session support system

TL;DR

This paper presents a real-time system for improvised music accompaniment that predicts chords using Hidden Markov Models and Variable Order Markov Models, evaluated through accuracy and user feedback.

Contribution

It introduces a novel real-time chord prediction system combining HMM and VOMM, with implementation and evaluation in musical improvisation contexts.

Findings

Achieved high prediction accuracy in real-time scenarios

System received positive subjective feedback from users

Demonstrated effective learning and prediction of musical structure

Abstract

We propose a method for the problem of real time chord accompaniment of improvised music. Our implementation can learn an underlying structure of the musical performance and predict next chord. The system uses Hidden Markov Model to find the most probable chord sequence for the played melody and then a Variable Order Markov Model is used to a) learn the structure (if any) and b) predict next chord. We implemented our system in Java and MAX/Msp and compared and evaluated using objective (prediction accuracy) and subjective (questionnaire) evaluation methods.