Towards Affective Algorithmic Composition

Automated systems for the selective adjustment of emotional responses by means of musical features are driving an emerging field: affective algorithmic composition. Strategies for algorithmic composition, and the large variety of systems for computerautomation of such strategies, are well documented in literature. Reviews of computer systems for expressive performance (CSEMPs) also provide a thorough overview of the extensive work carried out in the area of expressive computer music performance, with some crossover between composition and performance systems. Although there has been a significant amount of work (largely carried out within the last decade) implementing systems for algorithmic composition with the intention of targeting specific emotional responses in the listener, a full review of this work is not currently available, creating a shared obstacle to those entering the field which, if left unchecked, can only continue to grow. This paper gives an overview of the progress in this emerging field, including systems that combine composition and expressive performance metrics. Re-composition, and transformative algorithmic composition systems are included and differentiated where appropriate, highlighting the challenges these systems now face and suggesting a direction for further work. A framework for the categorisation and evaluation of these systems is proposed including methods for the parameterisation of musical features from semiotic research targeting specific emotional correlates. The framework provides an overarching epistemological platform and practical vernacular for the development of future work using algorithmic composition and expressive performance systems to monitor and induce affective states in the listener.