Maarten Grachten

Personal Information

Professional Activity
2017–present

Independent Machine Learning Consultant. LinkedIn Profile

2016–2017

Senior Researcher at the Department of Computational Perception; Johannes Kepler University, Linz, Austria. Currently resident as a visiting researcher at the Artificial Intelligence Research Institute (IIIA), Bellaterra, Spain

2013–2016

Senior Researcher at the Intelligent Music Processing and Machine Learning Group of the Austrian Research Institute for Artificial Intelligence; Vienna, Austria. Involved in the following EU Projects:

  • Lrn2Cre8: Learning to Create. 2013–2016. Future and Emerging Technologies (FET) programme within the 7th Framework Programme for Research of the European Commission. Grant agreement no. 610859 (Principal Investigator)
  • PHENICX: Performances as Highly Enriched aNd Interactive Concert Experiences. 2013–2016. ICT programme within the 7th Framework Programme for Research of the European Commission. Grant agreement no. 601166 (Named Investigator)
2010Post Doctoral Researcher at the Institute of Psychoacoustics and Electronic Music (IPEM), University of Ghent.
2010–2013
2007–2009
Post Doctoral Researcher at the Department of Computational Perception; Johannes Kepler University, Linz, Austria
2006 Researcher at the Music Technology Group, Pompeu Fabra University, Barcelona, Spain
2001–2006 PhD Researcher at the Artificial Intelligence Research Institute (IIIA), Bellaterra, Spain
Education
Contact Email: maarten . leave.this.part.out grachten leave.this.part.out @ leave.this.part.out gmail . leave.this.part.out com
or LinkedIn

Publications (see also Google Scholar and Researchgate)


2020

  • M. Grachten, S. Lattner, E. Deruty (2020). BassNet: A Variational Gated Autoencoder for Conditional Generation of Bass Guitar Tracks with Learned Interactive Control. Applied Sciences, Special Issue "Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening", 10(18):6627. pdf bib online. See also the BassNet introduction page.

2019

  • M. Grachten, E. Deruty, A. Tanguy (2019). Auto-adaptive Resonance Equalization using Dilated Residual Networks. Proceedings of the 20th International Society for Music Information Retrieval Conference. Delft, The Netherlands. pdf bib
  • S. Lattner, M. Grachten (2019). High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2019), New Paltz, New York, U.S.A., October 20-23. pdf bib
  • M. Grachten, C. E. Cancino-Chacón, T. Gadermaier (2019). partitura: A Python Package for Handling Symbolic Musical Data. Late Breaking Demo at the 20th International Society for Music Information Retrieval Conference. Delft, The Netherlands. pdf

2018

  • C. E. Cancino Chacón, M. Grachten, W. Goebl, G. Widmer (2018). Computational Models of Expressive Music Performance: A Comprehensive and Critical Review. Frontiers in Digital Humanities (Section Digital Musicology). DOI: 10.3389/fdigh.2018.00025. pdf bib .
  • M. Grachten, E. Deruty, A. Tanguy (2018). Auto-adaptive Resonance Equalization using Dilated Residual Networks. arXiv:1807.08636. pdf
  • S. Lattner, M. Grachten, G. Widmer (2018). Learning Interval Representations from Polyphonic Music Sequences. Proceedings of the 19th International Society for Music Information Retrieval Conference. Paris, France. pdf bib.
  • S. Lattner, M. Grachten, G. Widmer (2018). A predictive model for music based on learned relative pitch representations. Proceedings of the 19th International Society for Music Information Retrieval Conference. Paris, France. pdf bib.
  • S. Lattner, M. Grachten, G. Widmer (2018). Imposing Higher-Level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints. Journal of Creative Music Systems. Vol. 2 (2). pdf bib
  • C. E. Cancino Chacón, M. Grachten (2018). A Computational Study of the Role of Tonal Tension in Expressive Piano Performance. 15th International Conference on Music Perception and Cognition/10th triennial conference of the European Society for the Cognitive Sciences of Music. pdf.
  • G. Velarde, T. Weyde, C. E. Cancino Chacón, D. Meredith, M. Grachten (2018). Convolution-based Classification of Audio and Symbolic Representations of Music. Journal of New Music Research. Vol. 47 (3). pp. 191–205. pdf bib.
  • B. Erdős, M. Grachten, P. Czermak, Z. Kovács (2018). Artificial neural network assisted spectrophotometric method for monitoring fructo-oligosaccharides production. Food and Bioprocess Technology. Vol. 11 (2). Springer. pp. 305–313. pdf (preprint). bib

2017

  • C. E. Cancino Chacón, M. Bonev, A. Durand, M. Grachten, A. Arzt, L. Bishop, W. Goebl, G. Widmer (2017). The Accompanion v0.1: An Expressive Accompaniment System. Late Breaking/Demo at the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China. pdf poster Supplementary material (including demo videos)
  • C. E. Cancino Chacón, D. R. W. Sears, M. Grachten, G. Widmer (2017). What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music. 10th International Workshop on Machine Learning and Music (MML2017). pp. 7–12 pdf bib
  • S. Lattner, M. Grachten, G. Widmer (2017). Learning Transformations of Musical Material using Gated Autoencoders. In Proceedings of the 2nd Conference on Computer Simulation of Musical Creativity. London, UK. pdf bib
  • C. E. Cancino Chacón, M. Grachten, K. Agres (2017). From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). Suzhou, China. pdf bib
  • S. Lattner, M. Grachten (2017). Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation. arXiv:1707.01357. pdf bib
  • C. E. Cancino Chacón, T. Gadermaier, G. Widmer, M. Grachten (2017). An Evaluation of Linear and Non-Linear Models of Expressive Dynamics in Classical Piano and Symphonic Music. Machine Learning. Vol. 106 (6). Springer. pp. 887–909. pdf bib
  • M. Grachten, C. E. Cancino Chacón, T. Gadermaier, G. Widmer (2017). Towards computer-assisted understanding of dynamics in symphonic music. IEEE Multimedia. Vol. 24 (1), pp. 36–46. pdf (author's accepted copy) bib
  • M. Grachten, C. E. Cancino Chacón (2017). Temporal dependencies in the expressive timing of classical piano performances. In the Routledge Companion of Embodied Music Interaction. M. Lesaffre, M. Leman and P. J. Maes (Eds.). pp. 362–371. In press. pdf (author's accepted copy) bib

2016

  • C. E. Cancino Chacón, M. Grachten (2016). The Basis Mixer: A Computational Romantic Pianist. Late Breaking/Demo at the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, USA. pdf bib (poster: pdf)
  • G. Velarde, T. Weyde, C. E. Cancino Chacón, D. Meredith, M. Grachten (2016). Composer Recognition based on 2D-Filtered Piano-Rolls. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, USA. pdf bib
  • T. Gadermaier, M. Grachten, C. E. Cancino Chacón (2016). Modeling Loudness Variations in Ensemble Performance. In Proceedings of the 2nd International Conference on New Music Concepts (ICNMC 2016). Treviso, Italy. pdf bib

2015

  • M. Grachten, C. E. Cancino Chacón (2015). Strategies for Conceptual Change in Convolutional Neural Networks. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2015-04. pdf bib
  • C. E. Cancino Chacón, M. Grachten (2015). An evaluation of score descriptors combined with non-linear models of expressive dynamics in music. In Proceedings of the 18th International Conference on Discovery Science (DS 2015). pp. 48-62. pdf bib
  • M, Gasser, A. Arzt, Th. Gadermaier, M. Grachten, G. Widmer (2015). Classical music on the web -- user interfaces and data representations. In Proceedings of the 16th International Society for Music Information Retrieval Conference. Malaga, Spain. pdf bib
  • S. Lattner, C. E. Cancino Chacón, M. Grachten (2015). Pseudo-supervised training improves unsupervised melody segmentation. In Proceedings of the International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina. pdf bib
  • A. Arzt, H. Frostel, Th. Gadermaier, M. Gasser, G. Widmer, M. Grachten (2015). Artificial Intelligence in the Concertgebouw. In Proceedings of the International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina. pdf bib
  • K. Agres, C. E. Cancino Chacón, M. Grachten, S. Lattner (2015). Harmonics co-occurrences bootstrap pitch and tonality perception in music: Evidence from a statistical unsupervised learning model. CogSci 2015: The annual meeting of the Cognitive Science Society. Pasadena, CA, USA. pdf bib
  • S. Lattner, M. Grachten, K. Agres, C. E. Cancino Chacón (2015). Probabilistic Segmentation of Musical Sequences using Restricted Boltzmann Machines. In Proceedings of the Fifth Biennial International Conference on Mathematics and Computation in Music (MCM2015), London, UK. pdf bib

2014

  • C. E. Cancino Chacón, S. Lattner, M. Grachten (2014). Developing tonal perception through unsupervised learning. In Proceedings of the 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan. pdf bib
  • S. van Herwaarden, M. Grachten, W. B. de Haas (2014). Predicting expressive dynamics in piano performances using neural networks. In Proceedings of the 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan. pdf bib
  • M. Grachten, C. E. Cancino Chacón, G. Widmer (2014). Analysis and prediction of expressive dynamics using Bayesian linear models. 1st international workshop on computer and robotic Systems for Automatic Music Performance (SAMP14), Venice, Italy. pdf bib
  • F. Krebs, F. Korzeniowski, M. Grachten, G. Widmer (2014). Unsupervised Learning and Refinement of Rhythmic Patterns for Beat and Downbeat Tracking. In Proceedings of the 22nd European Signal Processing Conference (EUSIPCO 2014). pdf
  • M. Grachten, F. Krebs (2014). An assessment of learned score features for modeling expressive dynamics in music. IEEE Transactions on Multimedia. Vol. 16 (5). pp. 1-8. DOI: 10.1109/TMM.2014.2311013. pdf (preprint) bib
  • C. E. Cancino Chacón, M. Grachten, G. Widmer (2014). Bayesian linear basis models with gaussian priors for musical expression. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2014-12. pdf

2013

  • M. Grachten, M. Gasser, G. Widmer, A. Arzt (2013). Automatic alignment of music performances with structural differences. In Proceedings of the 14th International Society for Music Information Retrieval Conference. Curitiba, Brasil. pdf bib
  • E. Gómez, M. Grachten, A. Hanjalic, J. Janer, S. Jordà, C. F. Julià, C. Liem, A. Martorell, M. Schedl, and G. Widmer (2013). PHENICX: Performances as Highly Enriched aNd Interactive Concert Experiences. in Proceedings of SMAC Stockholm Music Acoustics Conference 2013 and SMC Sound and Music Computing Conference 2013, Stockholm, Sweden. pdf bib
  • S. Flossmann, M. Grachten, G. Widmer (2013). Expressive Performance Rendering with Probabilistic Models. In Guide to Computing for Expressive Music Performance (A. Kirke, E. R. Miranda, Eds.). Springer. pp. 75-98. ISBN: 978-1-4471-4122-8. pdf

2012

  • M. Grachten, G. Widmer (2012). Linear basis models for prediction and analysis of musical expression. Journal of New Music Research. Vol. 41 (4), pp. 311-322. pdf (preprint) bib
  • Krebs, F., Grachten, M. (2012). Combining Score and Filter Based Models to Predict Tempo Fluctuations in Expressive Music Performances. In Proceedings of the 9th Sound and Music Computing Conference (SMC 2012), Copenhagen, Denmark. pp. 358-363. pdf bib
  • D. Amelynck, M. Grachten, L. Van Noorden, M. Leman (2012). Towards E-Motion Based Music Retrieval: A study of Affective Gesture Recognition. IEEE Transactions on Affective Computing. Vol. 3 (2), pp. 250-259. pdf (preprint) bib
  • D. Moelants, M. Demey, M. Grachten, C.F. Wu, M. Leman (2012). The Influence of an Audience on Performers: A Comparison Between Rehearsal and Concert Using Audio, Video and Movement Data. Journal of New Music Research, Vol. 41 (1), pp. 67-78. bib

2011

  • M. Grachten, G. Widmer (2011). Explaining expressive dynamics as a mixture of basis functions. In Proceedings of the Eighth Sound and Music Computing Conference (SMC). Padua, Italy. pdf bib
  • M. Grachten, G. Widmer (2011). A method to determine the contribution of annotated performance directives in music performances. In Proceedings of the International Symposium of Performance Science. Toronto, Canada. pp. 39-44 pdf bib
  • S. Flossmann, M. Grachten, G. Widmer (2011). Expressive Performance with Bayesian Networks and Linear Basis Models. Rencon Workshop 2011: Musical Performance Rendering Competition for computer systems. Extended abstract. pdf
  • Kovács, Z., Román, A., Vatai, G., Ittzés, A., Grachten, M., Czermak, P. (2011), Experimental and numerical investigations on whey desalination with nanofiltration. Food Industry - Milk and Dairy Products. Vol. 22 (1), pp. 3--7. ISSN 0353-6564. pdf

2010

  • M. Grachten, M. Demey, D. Moelants, M. Leman (2010). Analysis and automatic annotation of singer’s postures during concert and rehearsal. In Proceedings of the Seventh Sound and Music Computing Conference (SMC). Barcelona, Spain. pdf bib
  • M. Molina-Solana, M. Grachten, G. Widmer (2010). Evidence for Pianist-Specific Rubato Style in Chopin Nocturnes. In Proceedings of the Eleventh International Society for Music Information Retrieval Conference. Utrecht, The Netherlands. pdf
  • M. Molina-Solana, M. Grachten (2010). Nature versus Culture in ritardando performances. In Proceedings of the Conference on Interdisciplinary Musicology (CIM10). Sheffield, UK. pdf
  • S. Flossmann, W. Goebl, M. Grachten, B. Niedermayer, G. Widmer (2010). The Magaloff Project: An Interim Report. Journal of New Music Research. 39(4), 363-377. bib

2009

  • M. Grachten, M. Schedl, T. Pohle, G. Widmer (2009). The ISMIR cloud: A decade of ISMIR conferences at your fingertips. In Proceedings of the Tenth International Society for Music Information Retrieval Conference. Kobe, Japan. pdf bib poster
  • M. Grachten, G. Widmer (2009). Who is who in the end? Recognizing pianists by their final ritardandi. In proceedings of the Tenth International Society for Music Information Retrieval Conference. Kobe, Japan. pdf bib
  • M. Grachten, G. Widmer (2009). The kinematic rubato model as a means of studying final ritards across pieces and pianists. In Proceedings of the 6th Sound and Music Computing Conference. Porto, Portugal. pdf bib
  • S. Flossmann, M. Grachten, G. Widmer (2009). Expressive Performance Rendering: Introducing Performance Context. In Proceedings of the 6th Sound and Music Computing Conference. pdf bib
  • M. Grachten, W. Goebl, S. Flossmann, G. Widmer (2009). Phase-plane representation and visualization of gestural structure in expressive timing. Journal of New Music Research. Volume 38, Issue 2, pp. 183-195. pdf (preprint) bib
  • G. Widmer, S. Flossmann, M. Grachten, (2009). YQX plays Chopin. AI Magazine. Vol. 30, nr. 3. AAAI Press. pdf bib

2008

  • S. Flossmann, M. Grachten, G. Widmer (2008). Experimentally Investigating the Use of Score Features for Computational Models of Expressive Timing. In Proceedings of the 10th International Conference on Music Perception and Cognition (ICMPC10). Sapporo, Japan. pdf
  • M. Grachten, W. Goebl, S. Flossmann, G. Widmer (2008). Intuitive visualization of gestures in expressive timing: A case study on the final ritard. In Proceedings of the 10th International Conference on Music Perception and Cognition (ICMPC10). pp. 228-235. Sapporo, Japan. pdf bib
  • H. Purwins, P. Herrera, M. Grachten, A. Hazan, R. Marxer, X. Serra (2008). Computational Models of Music Perception and Cognition I: The Perceptual and Cognitive Processing Chain. Physics of Life Reviews, Volume 5, Issue 3, pp. 151-168. Elsevier. pdf bib
  • H. Purwins, M. Grachten, P. Herrera, A. Hazan, R. Marxer, X. Serra (2008). Computational Models of Music Perception and Cognition II: Domain-Specific Music Processing. Physics of Life Reviews, Volume 5, Issue 3, pp. 169-182. Elsevier. pdf bib
  • M. Grachten, W. Goebl, S. Flossmann, G. Widmer (2008). Phase-plane visualizations of gestural structure in expressive timing. In Proceedings of the Fourth Conference on Interdisciplinary Musicology (CIM08). Thessaloniki, Greece. pdf bib

2007

  • M. Grachten, G. Widmer (2007). Towards Phrase Structure Reconstruction from Expressive Performance Data. In Proceedings of the International Conference on Music Communication Science (ICOMCS). pp. 56-59. Sydney, Australia. pdf bib
  • R. López de Mántaras, M. Grachten, J.L. Arcos (2007). Expressivity-preserving Tempo Transformation for Music - A case.based approach. 29th Annual German Conference on Artificial Intelligence, Bremen, Germany. Lecture Notes in Artificial Intelligence, Vol. 4314, pp. 1-6. Springer. pdf
  • R. López de Mántaras, M. Grachten, J.L. Arcos (2007). Playing with cases: Tempo transformations of Jazz performances using Case-Based Reasoning. Invited paper at FLAIRS'07: 20th International FLAIRS Conference, Key West, Florida, May 5-9, 2007. AAAI Press, 8-11. pdf
  • M. Grachten, R. Marxer, A. Hazan, H. Purwins (2007). Attention as musical interplay of bottom-up accents and expectation. In proceedings of the Gospel Workshop on Biologically Inspired Signal Processing, Barcelona, Spain. Extended Abstract.
  • A. Hazan, P. Herrera, R. Marxer, M. Grachten, H. Purwins (2007). Computational Modeling of Statistical Learning of Tone Sequences. Proceedings of 11th Intl. Conference on Cognitive and Neural Systems; Boston , Massachusetts, USA.

2006

  • M. Grachten (2006). Expressivity-aware Tempo Transformations of Music Performances Using Case Based Reasoning. PhD Thesis. Pompeu Fabra University. Barcelona, Spain. ISBN: 635-07-094-0. pdf bib
  • A. Hazan, M. Grachten, R. Ramirez (2006). Evolving performance models by performance similarity: Beyond note-to-note transformations. Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR06). Victoria, Canada. pdf bib
  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2006). TempoExpress: An expressivity-preserving musical tempo transformation system. Proceedings of the 21st National Conference on Artificial Intelligence}. AAAI-06, Boston, MA. pdf
  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2006). A Case Based Approach to Expressivity-aware Tempo Transformation. Machine Learning Journal. Vol. 65, nr. 2-3. pp. 411-437. Springer Netherlands pdf bib

2005

  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2005). Melody Retrieval using the Implication/Realization Model. Online Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR05). Extended abstract. pdf bib
  • M. Grachten, F. A. García, J. Ll. Arcos (2005). Navigating through Case Base Competence. Proceedings of ICCBR05: LNCS 3620. pdf bib

2004

  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2004). Melodic similarity: Looking for a good abstraction level. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR04). Pompeu Fabra University, Barcelona, Spain. pp. 210–215. pdf bib
  • M. Grachten, J. Ll. Arcos (2004). Music Performance Generation as Time Series Prediction, Proceedings of the ECCBR04 Workshops, Madrid, Spain. pdf
  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2004). TempoExpress, a CBR Approach to Musical Tempo Transformations. In Advances in Case-Based Reasoning. Proceedings of the 7th European Conference, ECCBR04, Lecture Notes in Computer Science. Springer. pdf bib
  • M. Grachten, J. Ll. Arcos (2004) Using the Implication/Realization Model for Measuring Melodic Similarity. In Proceedings of the 16th European Conference on Artificial Intelligence, ECAI04. IOS Press. pdf bib
  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2004). Evolutionary Optimization of Music Performance Annotation. Computer Music Modeling and Retrieval (CMMR04). Esbjerg, Denmark. Springer. pdf bib
  • M. Grachten (2004). Global Musical Tempo Transformations using Case Based Reasoning. Doctoral pre-thesis work, Pompeu Fabra University, Barcelona. pdf

2003

  • E. Gómez, M. Grachten, X. Amatriain, J.Ll. Arcos (2003). Melodic characterization of monophonic recordings for expressive tempo transformations. SMAC03. Stockholm, Sweden. pdf bib
  • J. Ll. Arcos, M. Grachten, R. López de Mántaras (2003). Extracting Performers' Behaviors to Annotate Cases in a CBR System for Musical Tempo Transformations. ICCBR 2003: LNCS pp. 20-34. Trondheim, Norway pdf bib Best paper award!
  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2003). Tempo-Express: tempo transformations preserving musical expression. Rencon Workshop of IJCAI03. Acapulco, Mexico. pdf bib

2002

  • M. Grachten, J. Ll. Arcos, R. López de Mántaras (2002). A Comparison of Different Approaches to Melodic Similarity. ICMAI02, Edinburgh, UK. pdf bib ps.zip
  • A summary of Gilles Gonon's seminar about his dissertation topic: "An Adaptive Transform Based on Entropic Criteria. Application to audio signals." (Universitat Pompeu Fabra, Barcelona, 19-12-2002).

2001

  • M. Grachten (2001) JIG: Jazz Improvisation Generator. Proceedings of the MOSART Workshop on Current Research Directions in Computer Music, Barcelona, Spain. pdf
  • A summary of the Music Performance Panel, helt at the MOSART Workshop 2001.

Awards