Music improvisation is fascinating to study, being essentially a live demonstration of a creative process. In jazz, musicians often improvise across predefined chord progressions (leadsheets). How do we assess the creativity of jazz improvisations? And can we capture this in automated metrics for creativity for current LLM-based generative systems? Demonstration of emotional involvement is closely linked with creativity in improvisation. Analysing musical audio, can we detect emotional involvement? This study hypothesises that if an improvisation contains more evidence of emotion-laden content, it is more likely to be recognised as creative. An embeddings-based method is proposed for capturing the emotional content in musical improvisations, using a psychologically-grounded classification of musical characteristics associated with emotions. Resulting ‘emovectors’ are analysed to test the above hypothesis, comparing across multiple improvisations. Capturing emotional content in this quantifiable way can contribute towards new metrics for creativity evaluation that can be applied at scale.

A significant test for software is to task it with replicating human performance, as done recently with creative software and the commercial project Beyond the Fence (undertaken for a television documentary Computer Says Show). The remit of this project was to use computer software as much as possible to produce “the world's first computer-generated musical”. Several creative systems were used to generate this musical, which was performed in London's West End in 2016. This paper considers the challenge of evaluating this project. Current computational creativity evaluation methods are ill-suited to evaluating projects that involve creative input from multiple systems and people. Following recent inspiration within computational creativity research from interaction design, here the DECIDE evaluation framework is applied to evaluate the Beyond the Fence project. Evaluation finds that the project was reasonably successful at achieving the task of using computational generation to produce a credible musical. Lessons have been learned for future computational creativity projects though, particularly for affording creative software more agency and enabling software to interact with other creative partners. Upon reflection, the DECIDE framework emerges as a useful evaluation “checklist” (if not a tangible operational methodology) for evaluating multiple creative systems participating in a creative task.

Computational creativity is the modelling, simulating or replicating of creativity computationally. In examining and learning from these “creative systems”, from what perspective should the creativity of a system be considered? Are we interested in the creativity of the system's output? Or of its creative processes? Features of the system? Or how it operates within its environment? Traditionally computational creativity has focused more on creative systems' products or processes, though this focus has widened recently. Creativity research offers the Four Ps of creativity: Person/Producer, Product, Process and Press/Environment. This paper presents the Four Ps, explaining each in the context of creativity research and how it relates to computational creativity. To illustrate the usefulness of the Four Ps in taking broader perspectives on creativity in its computational treatment, the concepts of novelty and value are explored using the Four Ps, highlighting aspects of novelty and value that may otherwise be overlooked. Analysis of recent research in computational creativity finds that although each of the Four Ps appears in the body of computational creativity work, individual pieces of work often do not acknowledge all Four Ps, missing opportunities to widen their work's relevance. We can see, though, that high-status computational creativity papers do typically address all Four Ps. This paper argues that the broader views of creativity afforded by the Four Ps is vital in guiding us towards more comprehensively useful computational investigations of creativity.

Can a computer be creative? And what can we learn about our own creativity from studying computational creativity? My research offers a comprehensive and practically useful investigation into how to evaluate the level of creativity demonstrated by computational systems. How should something so subjective as creativity be measured? I argue that it is most productive to treat creativity as a collection of inter-related factors such as originality, value and productivity, which are more tightly defined and therefore more amenable to measurement. Potential factors are being derived from empirical studies examining a wide variety of our writings on creativity. These will be tested in a simulation of a creative environment: the best performing factors will be applied to evaluation of existing creative systems, in comparison to assessments made by human judges. The aim of this is to identify key components for creativity, giving insight into how to approach the evaluation and improvement of computational systems and also towards human creativity as well.