Towards autonomous agents for live computer music : realtime machine listening and interactive music systems

This thesis will describe a set of interactive systems developed for a range of musical styles and instruments, all of which attempt to participate in a concert by means of audio signal analysis alone. Machine listening, being the simulation of human peripheral auditory abilities, and the hypothetical modelling of central auditory and cognitive processes, is utilised in these systems to track musical activity. Whereas much of this modelling is inspired by a bid to emulate human abilities, strategies diverging from plausible human physiological mechanisms are often employed, leading to matching capabilities which exceed or differ from the human counterparts. Technology is described which detects events from an audio stream, further analysing the discovered events (typically notes) for perceptual features of loudness, pitch, attack time and timbre. In order to exploit processes that underlie common musical practice, beat tracking is investigated, allowing the inference of metrical structure which can act as a co-ordinative framework for interaction. Psychological experiments into human judgement of perceptual attack time and beat tracking to ecologically valid stimuli clarify the parameters and constructs that should most appropriately be instantiated in the computational systems. All the technology produced is intended for the demanding environment of realtime concert use. In particular, an algorithmic audio splicing and analysis library called BBCut2 is described, designed with appropriate processing and scheduling facilities for realtime operation. Proceeding to outlines of compositional applications, novel interactive music systems are introduced which have been tested in real concerts. These are evaluated by interviews with the musicians who performed with them, and an assessment of their claims to agency in the sense of ‘autonomous agents’. The thesis closes by considering all that has been built, and the possibilities for future advances allied to artificial intelligence and signal processing technology.EThOS - Electronic Theses Online ServiceGBUnited Kingdo