Automatic estimation of harmonic tension by distributed representation of chords
This provides a veridical computational model for music informatics applications, though it is incremental as it builds on existing word2vec-type methods for a specific domain.
The paper tackles the problem of computationally modeling perceived harmonic tension in music by proposing a new approach based on distributed chord representations, and it shows that the model's predictions align well with empirical data from human listeners.
The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model's predictions conform very well with empirical evidence obtained from human listeners.