Geraint Wiggins

Geraint Wiggins is a researcher at Goldsmiths, University of London, specializing in computational creativity, music cognition, and artificial intelligence applications in music. His work bridges computational models with neuroscientific approaches to explore creativity and musical understanding. He has contributed to studies on musical expectation, statistical learning, and the cognitive mechanisms behind musical perception. His research often intersects disciplines like neuroscience, psychology, and machine learning, with a focus on developing algorithms that model human musical behavior.

Wiggins' publications span articles in journals like NeuroImage and New Generation Computing, as well as contributions to edited volumes and conference proceedings. His work includes pioneering studies on melodic segmentation, computational models of composition, and the analysis of minimalist music. Collaborations with experts in neuroscience, psychology, and musicology have enriched his interdisciplinary approach to understanding creativity and auditory perception.

He has presented at conferences such as the International Conference on Computational Creativity and the Society for Music Perception and Cognition. His research also explores the application of machine learning in music performance interfaces and the empirical validation of music theoretical frameworks through computational experiments.