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 | 86 | 2018 |
YQX plays Chopin G Widmer, S Flossmann, M Grachten AI magazine 30 (3), 35-35, 2009 | 74 | 2009 |
Automatic alignment of music performances with structural differences M Grachten, M Gasser, A Arzt, G Widmer Proceedings of International Society for Music Information Retrieval …, 2013 | 68 | 2013 |
Melody retrieval using the implication/realization model M Grachten, JL Arcos Rosell, R López de Mántaras | 65 | 2005 |
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 | 64 | 2018 |
Melodic similarity: Looking for a good abstraction level M Grachten, JL Arcos, RL De Mántaras 5th International Conference on Music Information Retrieval, 2004 | 59 | 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 | 58 | 2012 |
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 | 53 | 2017 |
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 | 53 | 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 | 49 | 2008 |
Expressive performance rendering: Introducing performance context S Flossmann, M Grachten, G Widmer Proceedings of the SMC, 155-160, 2009 | 38 | 2009 |
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 | 37 | 2019 |
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 | 37 | 2008 |
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 | 35 | 2012 |
Expressive performance rendering with probabilistic models S Flossmann, M Grachten, G Widmer Guide to Computing for Expressive Music Performance, 75-98, 2013 | 31 | 2013 |
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 | 31 | 2003 |
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 | 30 | 2015 |
A case based approach to expressivity-aware tempo transformation M Grachten, JL Arcos, RL de Mántaras Machine Learning 65, 411-437, 2006 | 30 | 2006 |
An assessment of learned score features for modeling expressive dynamics in music M Grachten, F Krebs IEEE Transactions on Multimedia 16 (5), 1211-1218, 2014 | 29 | 2014 |
The ISMIR Cloud: A Decade of ISMIR Conferences at Your Fingertips. M Grachten, M Schedl, T Pohle, G Widmer ISMIR, 63-68, 2009 | 29 | 2009 |