Shlomo Dubnov, Vignesh Gokul, Gérard Assayag. Switching Machine Improvisation Models by Latent Transfer Entropy Criteria. Physical Sciences Forum, 2023, 5 (1), pp.49. ff10.3390/psf2022005049ff.ffhal-04010744
Abstract: Music improvisation is the ability of musical generative systems to interact with either another music agent or a human improviser. This is a challenging task, as it is not trivial to define a quantitative measure that evaluates the creativity of the musical agent. It is also not feasible to create huge paired corpora of agents interacting with each other to train a critic system. In this paper we consider the problem of controlling machine improvisation by switching between several pre-trained models by finding the best match to an external control signal. We introduce a measure SymTE that searches for the best transfer entropy between representations of the generated and control signals over multiple generative models.
