Emovectors: assessing emotional content in jazz improvisations for creativity evaluation

TL;DR

This paper introduces 'emovectors', an embeddings-based method to quantify emotional content in jazz improvisations, aiming to develop automated metrics for creativity assessment in musical AI systems.

Contribution

It proposes a novel approach to measure emotional involvement in jazz improvisations using psychologically-grounded musical features, advancing automated creativity evaluation.

Findings

Emovectors effectively capture emotional content in jazz improvisations.

Higher emotional content correlates with perceived creativity.

Method enables scalable, automated assessment of musical creativity.

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.