Personalising behaviours of the multi-agent autonomous looper with a corpus of annotated co-performances

Abstract This article introduces a method to personalise the behaviour of a music co-improvisation system using a genetic algorithm that iteratively identifies optimal configurations governing the system’s behaviour according to a musician’s aesthetic objectives, using a corpus of annotated co-performances. The corpus annotations represent the decisions the semi-autonomous system should learn to make in different musical contexts, reflecting the musicians’ styles, ideas and aesthetic preferences. We apply this approach to personalise the multi-agent autonomous looper (MAAL), a co-improvisation system that autonomously samples and loops segments of an improvised performance by integrating machine listening with a rule-based framework. After detailing the design of the evolutionary search algorithm, we present the results of a study involving five expert improvising musicians. These musicians applied the proposed method to personalise the MAAL and co-improvised with it. We assessed the extent to which the MAAL’s behaviour, adapted to their personalised data, aligned with their individual aesthetic preferences. Findings show that automating the functions of sampling and layering loops seems to invite explorative and responsive creativity and that personalisation can facilitate co-adaptation between musicians and artificial agents by establishing a defined aesthetic vocabulary of co-creative behaviours.