Neural computing: an introduction
Neural computing: an introduction
Machine Learning
Psychoacoustically informed spectrography and timbre
Organised Sound
Synthesising timbres and timbre-changes from adjectives/adverbs
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Real-time feature-based synthesis for live musical performance
NIME '07 Proceedings of the 7th international conference on New interfaces for musical expression
An Automated Music Improviser Using a Genetic Algorithm Driven Synthesis Engine
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Identification of perceptual qualities in textural sounds using the repertory grid method
Proceedings of the 6th Audio Mostly Conference: A Conference on Interaction with Sound
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How can we provide interfaces to synthesis algorithms that will allow us to manipulate timbre directly, using the same timbre-words that are used by human musicians to communicate about timbre? This paper describes ongoing work that uses machine learning methods (principally genetic algorithms and neural networks) to learn (1) to recognise timbral characteristics of sound and (2) to adjust timbral characteristics of existing synthesized sounds.