Maarten Grachten
Maarten Grachten
Independent Machine Learning Consultant
Verified email at - Homepage
Cited by
Cited by
Imposing Higher-Level Structure in Polyphonic Music Generation Using Convolutional Restricted Boltzmann Machines and Constraints
S Lattner, M Grachten, G Widmer
Journal of Creative Music Systems 2 (2), 2018
YQX plays Chopin
G Widmer, S Flossmann, M Grachten
AI magazine 30 (3), 35-35, 2009
Melody retrieval using the implication/realization model
M Grachten, JL Arcos Rosell, R López de Mántaras
Automatic alignment of music performances with structural differences
M Grachten, M Gasser, A Arzt, G Widmer
Melodic similarity: Looking for a good abstraction level
M Grachten, JL Arcos, RL De Mántaras
5th International Conference on Music Information Retrieval, 2004
Linear basis models for prediction and analysis of musical expression
M Grachten, G Widmer
Journal of New Music Research 41 (4), 311-322, 2012
The Magaloff project: An interim report
S Flossmann, W Goebl, M Grachten, B Niedermayer, G Widmer
Journal of New Music Research 39 (4), 363-377, 2010
Computational models of music perception and cognition I: The perceptual and cognitive processing chain
H Purwins, P Herrera, M Grachten, A Hazan, R Marxer, X Serra
Physics of Life Reviews 5 (3), 151-168, 2008
Computational models of expressive music performance: A comprehensive and critical review
CE Cancino-Chacón, M Grachten, W Goebl, G Widmer
Frontiers in Digital Humanities 5, 25, 2018
Expressive performance rendering: Introducing performance context
S Flossmann, M Grachten, G Widmer
Proceedings of the SMC, 155-160, 2009
An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music
CE Cancino-Chacón, T Gadermaier, G Widmer, M Grachten
Machine Learning 106, 887-909, 2017
Melodic characterization of monophonic recordings for expressive tempo transformations
E Gómez, M Grachten, X Amatriain, JL Arcos
Proceedings of Stockholm Music Acoustics Conference 2003, 2003
Computational models of music perception and cognition II: Domain-specific music processing
H Purwins, M Grachten, P Herrera, A Hazan, R Marxer, X Serra
Physics of Life Reviews 5 (3), 169-182, 2008
The ISMIR Cloud: A Decade of ISMIR Conferences at Your Fingertips.
M Grachten, M Schedl, T Pohle, G Widmer
ISMIR, 63-68, 2009
A case based approach to expressivity-aware tempo transformation
M Grachten, JL Arcos, RL de Mántaras
Machine Learning 65, 411-437, 2006
The influence of an audience on performers: a comparison between rehearsal and concert using audio, video and movement data
D Moelants, M Demey, M Grachten, CF Wu, M Leman
Journal of New Music Research 41 (1), 67-78, 2012
Artificial intelligence in the concertgebouw
A Arzt, H Frostel, T Gadermaier, M Gasser, M Grachten, G Widmer
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
High-level control of drum track generation using learned patterns of rhythmic interaction
S Lattner, M Grachten
2019 IEEE Workshop on Applications of Signal Processing to Audio and …, 2019
Classical Music on the Web-User Interfaces and Data Representations.
M Gasser, A Arzt, T Gadermaier, M Grachten, G Widmer
ISMIR, 571-577, 2015
Toward e-motion-based music retrieval a study of affective gesture recognition
D Amelynck, M Grachten, L Van Noorden, M Leman
IEEE transactions on affective computing 3 (2), 250-259, 2011
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