Geraint Wiggins

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Marcus PearceQueen Mary University of London確認したメールアドレス: qmul.ac.uk
Daniel MüllensiefenProfessor, Department of Psychology, Goldsmiths, University of London確認したメールアドレス: gold.ac.uk
David MeredithAssociate Professor, Aalborg University確認したメールアドレス: create.aau.dk
Jamie ForthGoldsmiths, University of London確認したメールアドレス: gold.ac.uk
Alex McLeanThen Try This確認したメールアドレス: thentrythis.org
Kjell LemstromUniversity of Helsinki, Department of Computer Science確認したメールアドレス: helsinki.fi
Matthew PurverProfessor of Computational Linguistics, Queen Mary University of London / Jožef Stefan Institute確認したメールアドレス: qmul.ac.uk
Joy BhattacharyaProfessor, Department of Psychology, Goldsmiths University of London確認したメールアドレス: gold.ac.uk
George FazekasQueen Mary University of London確認したメールアドレス: qmul.ac.uk
Somnuk Phon-AmnuaisukProfessor, Universiti Teknologi Brunei確認したメールアドレス: utb.edu.bn
Kat AgresNational University of Singapore確認したメールアドレス: nus.edu.sg
Christophe RhodesGoogle UK Ltd確認したメールアドレス: google.com
Mark SandlerQueen Mary University of London確認したメールアドレス: qmul.ac.uk
Martin RohrmeierAssociate Professor of Digital Musicology, École Polytechnique Fédérale de Lausanne確認したメールアドレス: epfl.ch
Constance ScharffProfessor of Animal Behavior, Freie Universität Berlin確認したメールアドレス: zedat.fu-berlin.de
Stephen McAdamsMcGill University確認したメールアドレス: mcgill.ca
Florian ThalmannKyoto University確認したメールアドレス: umn.edu
Chris MellishComputing Science, University of Aberdeen確認したメールアドレス: abdn.ac.uk
Amílcar CardosoUniversity of Coimbra確認したメールアドレス: dei.uc.pt
Nick HarleyPostdoc, Vrije Universiteit Brussel確認したメールアドレス: vub.be

フォロー

Geraint Wiggins

Professor of Computational Creativity, Vrije Universiteit Brussel / Queen Mary University of London

確認したメールアドレス: vub.ac.be

Computational Creativity Music Cognition Cognitive Science Artificial Intelligence


タイトル引用回数順公開年順タイトル順	引用先引用先	年
Computational creativity: The final frontier? S Colton, GA Wiggins ECAI 2012, 21-26, 2012	683	2012
A preliminary framework for description, analysis and comparison of creative systems GA Wiggins Knowledge-based systems 19 (7), 449-458, 2006	483	2006
Expectation in melody: The influence of context and learning MT Pearce, GA Wiggins Music Perception 23 (5), 377-405, 2006	372	2006
AI methods for algorithmic composition: A survey, a critical view and future prospects G Papadopoulos, G Wiggins AISB symposium on musical creativity 124, 110-117, 1999	340	1999
Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation MT Pearce, MH Ruiz, S Kapasi, GA Wiggins, J Bhattacharya NeuroImage 50 (1), 302-313, 2010	296	2010
Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music D Meredith, K Lemström, GA Wiggins Journal of New Music Research 31 (4), 321-345, 2002	294	2002
Auditory expectation: the information dynamics of music perception and cognition MT Pearce, GA Wiggins Topics in cognitive science 4 (4), 625-652, 2012	284	2012
Searching for computational creativity GA Wiggins New Generation Computing 24 (3), 209-222, 2006	255	2006
Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music H Egermann, MT Pearce, GA Wiggins, S McAdams Cognitive, Affective, & Behavioral Neuroscience 13, 533-553, 2013	237	2013
Musical creativity: multidisciplinary research in theory and practice I Delège, GA Wiggins Psychology Press, 2006	209	2006
Improved methods for statistical modelling of monophonic music M Pearce, G Wiggins Journal of New Music Research 33 (4), 367-385, 2004	174	2004
Evolutionary methods for musical composition G Wiggins, G Papadopoulos, S Phon-Amnuaisuk, A Tuson ICANNGA, 1998	154	1998
The role of expectation and probabilistic learning in auditory boundary perception: A model comparison MT Pearce, D Müllensiefen, GA Wiggins Perception 39 (10), 1367-1391, 2010	146	2010
A framework for the evaluation of music representation systems G Wiggins, E Miranda, A Smaill, M Harris Computer Music Journal 17 (3), 31-42, 1993	142	1993
A genetic algorithm for the generation of jazz melodies G Wiggins, G Papadopoulos Proceedings of the finnish conference on artificial intelligence (STeP’98 …, 1998	139*	1998
Motivations and methodologies for automation of the compositional process M Pearce, D Meredith, G Wiggins Musicae Scientiae 6 (2), 119-147, 2002	133	2002
Statistical learning of harmonic movement D Ponsford, G Wiggins, C Mellish Journal of New Music Research 28 (2), 150-177, 1999	132	1999
Converging on the divergent: The history (and future) of the international joint workshops in computational creativity A Cardoso, T Veale, GA Wiggins AI magazine 30 (3), 15-15, 2009	122	2009
Towards a framework for the evaluation of machine compositions M Pearce, G Wiggins Proceedings of the AISB 1, 22-32, 2001	118	2001
The four-part harmonisation problem: a comparison between genetic algorithms and a rule-based system S Phon-Amnuaisuk, G Wiggins Proceedings of the AISB’99 Symposium on Musical Creativity, 28-34, 1999	111	1999

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