CARLOS EDUARDO CANCINO-CHACÓN


Assistant Professor
Institute of Computational Perception
Johannes Kepler University Linz
Wiesingerstraße 4, 2. Floor, Room 250
1010 Vienna, Austria

Email: carlos_eduardo.cancino_chacon@jku.at



RESEARCH INTERESTS

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



CURRICULUM VITAE

EDUCATION

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

Supervisor: Gerhard Widmer
Co-supervisor: Maarten Grachten

2011 - 2014
Master's degree in Electrical Engineering and Audio Engineering,
Graz University of Technology/University of Music and Performing Arts Graz, Austria.

Supervisor: Franz Pernkopf

2006 - 2011
Undergraduate degree in Physics,
National Autonomous University of Mexico, Mexico City, Mexico.

Supervisor: Marcos Ley Koo.

1999 - 2011
Undergraduate degree in Piano Performance,
National Conservatory of Music, Mexico City, Mexico.

Supervisor: Héctor Alfonso Rojas Ramírez.

RESEARCH EXPERIENCE

2020 - present
Institute of Computational Perception
Johannes Kepler University Linz, Austria

Assistant Professor

2020 - 2021
RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion
University of Oslo, Norway

Guest Researcher

2018 - 2020
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

2021 -
Johannes Kepler University Linz, Austria.
Assistant Professor
Current Courses (WS23):
Musical Informatics (undergraduate/master's level)
Reinforcement Learning (undergraduate level)
Seminar in Artificial Intelligence (master's level)

Past Courses:
Seminar in Data Science (undergraduate's level)
Machine Learning and Pattern Classification (exercise track; undergraduate/master's level)
Artificial Intelligence (exercise track; undergraduate level)


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


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.

2021
Breakthrough of the Year (in the category Art and Science; as part of Gerhard Widmer's team),
Falling Walls Science Summit 2021.

Lead developer of the ACCompanion, an expressive computer accompaniment system, including a live demonstration.
(video of Gerhard Widmer's talk including live demo)


A pdf version of this CV can be found here.

PUBLICATIONS

PEER REVIEWED PUBLICATIONS

  • H. Zhang, E. Karystinaios, S. Dixon, G. Widmer and C. Cancino-Chacón (2023)
    “Symbolic Music Representations for Classification Tasks: A Systematic Evaluation”
    In Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR 2023), Milan, Italy. (pdf)

  • S. Peter, C. Cancino-Chacón, F. Foscarin, A. P. McLeod, F. Henkel, E. Karystinaios and G. Widmer (2023)
    “Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset.”
    Transactions of the International Society for Music Information Retrieval Vol. 6(1), pp. 27-42 (pdf) (link) (dataset)

  • C. Cancino-Chacón, S. Peter, P. Hu, E. Karystinaios, F. Henkel, F. Foscarin, N. Varga and G. Widmer (2023)
    “The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist”
    In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23), Macao, S. A. R. (pdf) (supplementary material) (github) (playlist)

  • C. Cancino-Chacón (2023)
    “Commentary on A Computational Approach to Detection and Prediction of (Ir)Regularity in Children's Folk Songs.”
    Empirical Musicology Review Vol. 16(2) (pdf) (link)

  • C. Cancino-Chacón, S. Peter, E. Karystinaios, F. Foscarin, M. Grachten and G. Widmer (2022)
    ”Partitura: A Python Package for Symbolic Music Processing”
    In Proceedings of the Music Encoding Conference (MEC2022), Halifax, Canada (pdf) (github)

  • F. Foscarin, E. Karystinaios, S. Peter, C. Cancino-Chacón, M. Grachten and G. Widmer (2022)
    ”The match file format: Encoding Alignments between Scores and Performances”
    In Proceedings of the Music Encoding Conference (MEC2022), Halifax, Canada (pdf)

  • L. Bishop, C. Cancino-Chacón, W. Goebl (2021)
    “Beyond synchronization: How and why do ensemble performers communicate” In Together in Music: Participation, Co-Ordination and Creativity in Ensembles. R. Timmers, F. Bailes and H. Daffern (eds).
    Oxford University Press. (authors' accepted copy)

  • C. Cancino-Chacón, S. Peter, S. Chowdhury, A. Aljanaki, G. Widmer (2020)
    “On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game”
    In Proceedings of the 21th International Society for Music Information Retrieval Conference (ISMIR 2020) Montreal, Canada. (pdf) (dataset)

  • O. Lartillot, C. Cancino-Chacón, C. Brazier (2020)
    “Real-Time Visualization of Fugue Played by a String Quartet”
    In Proceedings of the 17th Sound and Music Computing Conference (SMC2020) Torino, Italy. (pdf)

  • 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. (authors' 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. (authors' 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

  • C. Cancino-Chacón, S. Peter, G. Widmer (2022)
    “Can We Achieve Togetherness with an Artificial Partner? Insights and Challenges from Developing an Automatic Accompaniment System”
    Musical Togetherness Symposium (MTS), Vienna, Austria (pdf)

  • C. Cancino-Chacón, S. Peter, E. Karystinaios, G. Widmer (2021)
    “Towards Quantifying Differences in Expressive Piano Performances: Are Euclidean-like Distance Measures Enough?”
    Rhythm Production and Perception Workshop 2021 (RPPW2021), Oslo, Norway. (pdf)

  • C. Cancino-Chacón, S. Peter, S. Chowdhury, A. Aljanaki, G. Widmer (2021)
    “Sorting Musical Expression: Characterization of Descriptions of Expressive Piano Performances”
    16th International Conference on Music Perception and Cognition (ICMPC16-ESCOM11), Sheffield, UK. (pdf) (video) (interactive demo)

  • 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. Goebl (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. Cancino-Chacón (June 2023)
    “Towards Understanding Emotion Communicated Through Performance of Orchestral Music: Preliminary Results"”
    Invited Talk at the 2nd MIRAGE Symposium, RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway.

  • C. Cancino-Chacón, F. Foscarin, E. Karystinaios, S. Peter (December 2022)
    “An Introduction to Symbolic Music Processing with Partitura”
    Tutorial presented at the 23rd International Society for Music Information Retrieval Conference (ISMIR 2022), Bengaluru, India (abstract) (notebooks)

  • C. Cancino-Chacón (September 2022)
    “Play it Again, Hall 9000! Towards Expressive Computational Performers”
    Invited talk at the Max Plank Instutitute for Intelligent Systems, Tübingen, Germany.

  • O. Lartillot, E. Guldbransen, C. E. Cancino-Chacón (June 2021),
    “Dynamics analysis, and application to a comparative study of Bruckner performances”
    Invited Talk at the MIRAGE Symposium #1: Computational Musicology, RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway. (video)

  • C. E. Cancino-Chacón (April 2020),
    “I'll be Bach! Modeling Expressive Performance with Machine Learning”
    Invited Talk at the Food & Paper Talk Series, RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway. (abstract)

  • 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