Bown, Oliver and Lexer, Sebastian (2006) Continuous Time Recurrent Neural Networks for Generative and Interactive Musical Performance. In: EvoWorkshops2006: EvoMUSART; the 4th European Workshop on Evolutionary Music and Art, 10 - 12 April 2006, Budapest, Hungary.
|Type of Research:||Conference, Symposium or Workshop Item|
|Creators:||Bown, Oliver and Lexer, Sebastian|
‘Continuous-Time Recurrent Neural Networks for Generative and Interactive Musical Performance’ was a paper delivered at the 4th European Workshop on Evolutionary Music and Art, subsequently published in proceedings.
The paper begins by identifying the dominant paradigm located at the junction of computer music and artificial intelligence as one which identifies the goal of developing generative or interactive software agents which exhibit musicality.
Where once such a goal remained the preserve of dedicated research institutes with access to massively parallel computer networks, the authors demonstrate that this is no longer the case with increased computational capacity in consumer hardware and extensible music programmes such as Max/MSP, PD and Supercollider.
The researchers identify a problem with this potentially liberating convergence of raw number crunching and flexible programming languages: musicians tend to tailor their own dedicated solutions to their own perceived needs. Rather than follow this route, the researchers determined to engineer a generic behavioural tool that can be developed in different directions by different practising musicians, each with their own aesthetic instincts and compositional requirements.
The vehicle through which the researchers develop their solution is a specific form of artificial intelligence called a Continuous-Time Recurrent Neural Network. The researchers evoke a number of previous approaches that have sought to simulate complex systems in both analogue and computer-based settings. The advantage of their chosen model is that the AI is able to evolve in response to stimuli - in this case musical events – and for that evolution to function as a kind of training, meaning that the system can be programmed to adapt to different kinds of musicians’ work. The researchers argue that a CTRNN-based evolutionary music system is ideally suited to working with improvising musicians. They provide evidence for this argument, in the form of applied case studies.
|Additional Information (Publicly available):||
|Your affiliations with UAL:||Colleges > London College of Communication|
|Date:||4 March 2006|
|Related Websites:||http://www.olliebown.com/main_blog/?page_id=4, http://www.lcc.arts.ac.uk/41501.htm|
|Event Location:||Budapest, Hungary|
|Date Deposited:||03 Dec 2009 23:04|
|Last Modified:||18 Aug 2010 13:15|
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