Emovectors: assessing emotional content in jazz improvisations for creativity evaluation
This work addresses the need for automated metrics to evaluate creativity in jazz improvisations, which could be applied to LLM-based generative systems, though it appears incremental in developing new evaluation tools.
The study tackled the problem of assessing creativity in jazz improvisations by hypothesizing that emotional content indicates creativity, and proposed an embeddings-based method to quantify emotional content, resulting in 'emovectors' for analysis across multiple improvisations.
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.