Emovectors: assessing emotional content in jazz improvisations for creativity evaluation

TL;DR

Jazz improvisation offers a live testbed for creativity, yet scalable, automated creativity metrics remain scarce. The authors introduce emovectors, emotion embeddings derived from Juslin-Laukka acoustic cues grounded in psychological models, to quantify emotion in improvisations. Preliminary results indicate that more emotion-laden content accompanies highly regarded performances, supporting the hypothesis and enabling scalable evaluation across large data sets, including AI-generated outputs. Limitations include small samples and the need for validated creativity rankings, but the approach provides a practical path toward automated creativity assessment in music AI contexts.

Abstract

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.