What makes music so important, what can make a musical performance or concert so special and stirring? It is the things the music expresses, the emotions it induces, the associations it evokes, the drama and characters it portrays. The sources of this expressivity are manifold: the music itself, its structure, orchestration, personal associations, social settings, but also - and very importantly - the act of performance, the interpretation and expressive intentions made explicit by musicians through nuances in timing, dynamics etc. Thanks to research in fields like Music Information Research (MIR), computers can do many useful things with music, from beat and rhythm detection to song identification and tracking. However, they are still far from grasping the essence of music: they cannot tell whether a performance expresses playfulness or ennui, solemnity or gaiety, determination or uncertainty; they cannot produce music with a desired expressive quality; they cannot interact with human musicians in a truly musical way, recognising and responding to the expressive intentions implied in their playing.
The project is about developing machines that are aware of certain dimensions of expressivity, specifically in the domain of (classical) music, where expressivity is both essential and - at least as far as it relates to the act of performance - can be traced back to well-defined and measurable parametric dimensions (such as timing, dynamics, articulation). The project focuses on developing computer systems that can recognise and characterise music by expressive aspects, generate and react to expressive qualities in music. To do so, we need to (1) bring together the fields of AI, Machine Learning, Music Information Retrieval (MIR), and Music Performance Research; (2) integrate theories from musicology to build more well-founded models of music understanding; (3) support model learning and validation with massive musical corpora of a size and quality unprecedented in computational music research.
The resulting computer technologies include computational models of expressive piano performance (autonomous and interactive); deep neural networks that recognise intended emotions and expressive character in music recordings; systems that successfully track expressive performances in real time; and a multitude of computer models of musical structure perception - all of which will be useful for a wide variety of purposes, such as more refined music search and recommendation systems, or new musically 'sensitive' computer systems for interactive music making. A specific demonstrator we targeted from the start and which in the end was successfully developed and also presented to a wide audience, is the "ACCompanion": a computer that plays together with a human pianist in a musically natural and expressive way, recognising and anticipating the pianist's expressive intentions, and adapting its playing style so as to match the expressive quality of the music, making for a natural musical interaction and experience.
