Musical Expectation Modelling from Audio: a causal mid-level approach to predictive representation and learning of spectro-temporal events.

We develop in this thesis a computational model of music expectation, which may be one of the most important aspects in music listening. Many phenomenons related to music listening such as preference, surprise or emotions are linked to the anticipatory behaviour of listeners. In this thesis, we concentrate on a statistical account to music expectation, by modelling the processes of learning and predicting spectro-temporal regularities in a causal fashion.

The principle of statistical modelling of expectation can be applied to several music representations, from symbolic notation to audio signals. We first show that computational learning architectures can be used and evaluated to account behavioral data concerning auditory perception and learning. We then propose a what/when representation of musical events which enables to sequentially describe and learn the structure of acoustic units in musical audio signals.

The proposed representation is applied to describe and anticipate timbre features and musical rhythms. We suggest ways to exploit the properties of the expectation model in music analysis tasks such as structural segmentation. We finally explore the implications of our model for interactive music applications in the context of real-time transcription, concatenative synthesis, and visualization.