A fuzzy inductive approach for rule-based modelling of high level structures in algorithmic composition systems

Abstract

Algorithmic composition systems are now widely understood. However, its capacity for producing outputs consistently showing high level structures is still a field of research. In the present work, the Fuzzy Inductive Reasoning (FIR) methodology and an extension of it, the Linguistic rules in FIR (LR-FIR) are the main tools chosen for modeling such features. FIR/LR-FIR operates over the produced outputs of an algorithmic composition system, and through qualitative user evaluation is able to extract rules using configurations of low level characteristics that models high level features. Subsequently, the rules are used for the exploration of all possible outputs of an algorithmic system finding a subset of outputs showing the desired property. Finally extracted rules are evaluated and discussed in the context of musical knowledge.