CARLOS EDUARDO CANCINO-CHACÓN

Austrian Research Institute for Artificial Intelligence
Freyung 6/6
1010 Vienna, Austria

Email: carlos.cancino@ofai.at



RESEARCH INTERESTS

Computational Models of Expressive Music Performance
Human-Computer Interaction in Music
Cognitively-plausible Computational Models of Music Analysis
Music Information Retrieval
Machine Learning (Deep Learning, Probabilistic Graphical Models)



CURRICULUM VITAE

EDUCATION

2014 - 2018
PhD. in Computer Science,
Johannes Kepler University of Linz, Austria.

Supervisor: Gerhard Widmer
Co-supervisor: Maarten Grachten

2011 - 2014
M. Sc. in Electrical Engineering and Audio Engineering,
Graz University of Technology/University of Music and Performing Arts Graz, Austria.

Supervisor: Franz Pernkopf

2006 - 2011
Licenciatura como Físico,
National Autonomous University of Mexico, Mexico City, Mexico.

Supervisor: Marcos Ley Koo.

1999 - 2011
Licenciatura en Concertista de Piano,
National Conservatory of Music, Mexico City, Mexico.

Supervisor: Héctor Alfonso Rojas Ramírez.

RESEARCH EXPERIENCE

2020 - present
RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion
University of Oslo, Norway

Guest Researcher

2018 - present
Austrian Research Institute for Artificial Intelligence, Vienna, Austria
Intelligent Music Processing and Machine Learning Group

Posdoctoral Researcher

2014 - 2018
Austrian Research Institute for Artificial Intelligence, Vienna, Austria
Intelligent Music Processing and Machine Learning Group

Predoctoral Researcher



TEACHING EXPERIENCE

2011
National Conservatory of Music, Mexico City, Mexico.
Course Lecturer (Level B)
Courses: Elementary Music Theory I and Harmony (Levels I-III)


SCHOLARSHIPS AND AWARDS

2012 - 2014
Fundación INBA – CONACYT Scholarship of the Mexican National Council for Science and Technology (CONACyT)

2017
Award for Creative Achievement at the AccompaniX Competition,
2017 Turing Tests in the Creative Arts.

$500 team award for development of an expressive computer accompaniment system.

A pdf version of this CV can be found here.

PUBLICATIONS

PEER REVIEWED PUBLICATIONS

  • F. Simonetta, C. Cancino-Chacón, S. Ntalampiras, G. Widmer (2019)
    “A Convolutional Approach to Melody Line Identification in Symbolic Scores”
    In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR 2019) Delft, The Netherlands. (pdf) (supplementary materials)

  • L. Bishop, C. Cancino-Chacón, W. Goebl (2019)
    “Moving to Coordinate, Moving to Interact: Patterns of Body Motion in Musical Duo Performance”.
    Music Perception. (link) (pdf)

  • L. Bishop, C. Cancino-Chacón, W. Goebl (2019)
    “Eye gaze as a means of giving and seeking information during musical interaction”.
    Consciousness and Cognition. (link) (pdf)

  • C. E. Cancino-Chacón, M. Gracthen, W. Goebl, G. Widmer (2018)
    “Computational Models of Expressive Music Performance: A Comprehensive and Critical Review”.
    Frontiers in Digital Humanities. (link) (pdf)

  • G. Velarde, C. Cancino Chacón, D. Meredith, T. Weyde, M. Grachten (2018)
    “Convolution-based classification of audio and symbolic representations of music”.
    Journal of New Music Research. (link)

  • C. E. Cancino-Chacón, M. Grachten, D. R. W. Sears, G. Widmer (2017).
    “What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music”.
    In Proceedings of the 10th International Workshop on Machine Learning and Music (MML 2017). Barcelona, Spain. (pdf)

  • 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) (supplementary materials)

  • 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)

  • 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)

  • 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. Lessafre, M. Leman and P. J. Maes (Eds). Routledge. pp. 362-371. (pdf)(autor's accepted copy)

  • 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 City, NY, USA. (pdf)

  • 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)

  • C. E. Cancino Chacón and 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 (DS2015). Banff, Canada. (pdf)

  • S. Lattner, C. E. Cancino Chacón and M. Grachten (2015).
    Pseudo-Supervised Training Improves Unsupervised Melody Segmentation”.
    In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). Buenos Aires, Argentina. (pdf)

  • S. Lattner, M. Grachten, K. Agres and 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)

  • K. Agres, C. E. Cancino Chacón, M. Grachten and S. Lattner (2015).
    Harmonics co-occurrences bootstrap pitch and tonality perception in music: Evidence from a statistical unsupervised learning model”.
    The Annual Meeting of the Cognitive Science Society (CogSci2015). Pasadena, CA, USA. (pdf)

  • C. E. Cancino Chacón, S. Lattner, M. Grachten (2014).
    “Developing tonal perception through unsupervised learning”.
    In Proceedings of the15th International Society for Music Information Retrieval Conference (ISMIR 2014). Taipei, Taiwan. (pdf)

  • C. E. Cancino Chacón and P. Mowlaee (2014).
    “Least Squares phase estimation of mixed signals”.
    In Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014). Singapore. (pdf)

  • M. Grachten, C. E. Cancino Chacón and G. Widmer (2014).
    Analysis and prediction of expressive dynamics using Bayesian linear models”.
    In Proceedings of the 1st International Workshop on computer and robotic Systems for Automatic Music Performance (SAMP14). Venice, Italy. (pdf)

  • S. Tschiatschek, C. E. Cancino Chacón, and F. Pernkopf (2013).
    Bounds for Bayesian Network Classifiers with Reduced Precision Parameters”,
    In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013). Vancouver, Canada. (pdf)

EXTENDED ABSTRACTS

  • M. Grachten, C. 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 (ISMIR 2019), Delft, The Netherlands. (pdf) (code) (documentation)

  • D. Weigl, C. Cancino-Chacón, M. Bonev, W. Goebl (2019),
    “Linking and Visualising Performance Data and Semantic Music Encodings in Real-time”
    Late Breaking/Demo at the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands. (pdf)

  • C. Cancino-Chacón, S. Balke, F. Krebs, C. Stussak, G. Widmer (2019),
    “The Con Espressione! Exhibit: Exploring Human-Machine Collaboration in Expressive Performance”
    Late Breaking/Demo at the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands. (pdf) (video) (code)

  • Z. Shi, C. Cancino-Chacón, G. Widmer (2019),
    “User Curated Shaping of Expressive Performances”
    Invited Talk at the ICML 2019 Workshop on Machine Learning for Music Discovery, 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA. (pdf)

  • C. E. Cancino-Chacón, M. Grachten (2018),
    “A Computational Study of the Role of Tonal Tension in Expressive Piano Performance”
    Proceedings of the 15th International Conference on Music Perception and Cognition (ICMPC15 ESCOM10), Graz, Austria. (proceedings pdf) (abstract pdf) (poster)

  • L. Bishop, C. E. Cancino-Chacón, W. Goebl (2018),
    “Visual Signals between Improvisers Indicate Attention rather than Intentions”
    Proceedings of the 15th International Conference on Music Perception and Cognition (ICMPC15 ESCOM10), Graz, Austria. (pdf)

  • C. E. Cancino-Chacón, M. Bonev, A. Durand, M. Grachten, A. Arzt, L. Bishop, W. Goebel, 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 materials)

  • L. Bishop, C. E. Cancino-Chacón, W. Goebel (2017),
    “Mapping Visual Attention of Duo Musicians During Rehearsal of Temporally-Ambiguous Music”.
    In Proceedings of the International Symposium on Performance Science (ISPS 2017), Reykjavik, Iceland. (pdf)

  • 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) (poster) (web app) (supplementary materials)

INVITED TALKS AND TUTORIALS

  • C. E. Cancino-Chacón (March 2020),
    “Machine Listening of Orchestral Recordings”
    Invited Talk at the Workshop on Musical Listening, University of Oslo, Norway.

  • C. E. Cancino-Chacón, K. Kosta, M. Grachten (November 2019),
    “Computational Modeling of Musical Expression: Perspectives, Datasets, Analysis and Generation”
    Tutorial presented at the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands. (abstract) (slides) (supplementary materials)

  • C. E. Cancino-Chacón (March 2019),
    “Modeling Expressive Music Performance with Non-linear Basis Function Models”
    Invited Talk at the Deep Learning Seminar, University of Vienna, Austria. (abstract)

  • C. E. Cancino-Chacón (January 2019),
    “Computational Modeling of Expressive Music Performance with Linear and Non-linear Basis Function Models”
    Invited Talk at the Austrian Research Institute for Artificial Intelligence, Vienna, Austria.

  • C. E. Cancino-Chacón (November 2016),
    “¿Escuchan los androides música electrónica?”
    Invited Talk at the Talk series: Pláticas DeMentes. Faculty of Psychology, National Autonomous University of Mexico. (abstract pdf)

  • C. E. Cancino-Chacón (November 2016),
    “En busca del factor Mozart”
    Invited Talk at the National Conservatory of Music. Mexico City, Mexico. (abstract pdf)

THESES

  • C. E. Cancino Chacón (2018),
    “Computational Modeling of Expressive Music Performance with Linear and Non-linear Basis Function Models”.
    Johannes Kepler University Linz, Austria. (pdf) (online extras)

  • C. E. Cancino Chacón (2014),
    “Tarkus Belief Propagation: On Message Passing Algorithms and Computational Commutative Algebra”.
    Graz University of Technology. Graz, Austria. (pdf)

  • C. E. Cancino Chacón (2011),
    “Análisis teórico experimental de transductores de ultrasonido tipo Langevin”.
    National Autonomous University of Mexico. Mexico City, Mexico. (pdf)

TECHNICAL REPORTS

  • C. E. Cancino Chacón, M. Grachten (2016)
    “Rendering Expressive Performances of Musical Pieces Through Sampling From Generative Probabilistic Models”
    Technical Report, Austrian Research Institute for Artificial Intelligence, Vienna, TR-2016-01. (pdf)

  • M. Grachten, C. E. Cancino Chacón (2015).
    Strategies for Conceptual Change in Convolutional Neural Networks”.
    Technical Report, Austrian Research Institute for Artificial Intelligence, Vienna, TR-2015-04. (pdf)

  • C. E. Cancino Chacón, M. Grachten, G. Widmer (2014),
    “Bayesian Linear Models with Gaussian Priors for Musical Expression”,
    Technical Report, Austrian Research Institute for Artificial Intelligence, Vienna, TR-2014-12. (pdf)

  • C. E. Cancino Chacón (2013),
    “Reduced Precision Bayesian Network Classifiers”,
    Laboratory for Signal Processing and Speech Communication, Graz University of Technology. Graz, Austria. (pdf)


Music