This is work in progress where we outline a design process for a computationally creative musical performance system using the Creative Systems Framework (CSF). The proposed system is intended to produce virtuosic interpretations, and subsequent synthesized renderings of these interpretations with a physical model of a bass guitar, using case-based reasoning and reflection. We introduce our interpretations of virtuosity and musical performance, outline the suitability of case-based reasoning in computationally creative systems and introduce notions of computational creativity and the CSF. We design our system by formalising the components of the CSF and briefly outline a potential implementation. In doing so, we demonstrate how the CSF can be used as a tool to aid in designing computationally creative musical performance systems. CCS CONCEPTS• Computing methodologies → Instance-based learning; • Applied computing → Sound and music computing;KEYWORDS computational creativity, expressive music performance, virtuosity, case-based reasoning ACM Reference Format:
Computationally creative systems require semantic information when reflecting or self reasoning on their output. In this paper we outline the design of a computationally creative musical performance system aimed at producing virtuosic interpretations of musical pieces and provide an overview of its implementation. The case-based reasoning part of the system relies on a measure of musical similarity based on the FANTASTIC and SynPy toolkits that provide melodic and syncopated rhythmic features, respectively. We conducted a listening test based on pair-wise comparison to assess to what extent the machine-based similarity models match human perception. We found the machine-based models to differ significantly to human responses due to differences in participants' responses. The best performing model relied on features from the FANTASTIC toolkit obtaining a rank match rate with human response of 63%, while features from the SynPy toolkit only obtained a ranking match rate of 46%. While more work is needed on a stronger model of similarity, we do not believe these results prevent FANTASTIC features being used as a measure of similarity within creative systems.
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