Department of Computational Perception

CON ESPRESSIONE
Towards Expressivity-aware Computer Systems in Music

ERC Advanced Grant 2015, European Research Council (ERC)

Call identifier: ERC-2014-AdG

Project Number: 670035

Principal Investigator: Gerhard Widmer

Project Period: 2016 – 2020

Funding Amount: € 2,318,750.00

Scientific Publications

• Widmer, G. (2016).
  Getting Closer to the Essence of Music: The Con Espressione Manifesto.
  ACM Transactions on Intelligent Systems and Technology 8(2), Article 19. DOI: 10.1145/2899004
  
  Link to final published version via ACM AuthorIzer:
  
  Getting Closer to the Essence of Music: The Con Espressione Manifesto

• Cancino Chacón, C., Grachten, M., Goebl, W., and Widmer G. (2018).
  Computational Models of Expressive Music Performance: A Comprehensive and Critical Review.
  Frontiers in Digital Humanities vol.5, article 25, 1-23.


• Cancino Chacón, C. (2018).
  Computational Modeling of Expressive Music Performance through Linear and Non-linear Basis Function Models.
  Ph.D. Thesis, Dept. of Computational Perception, Johannes Kepler University (JKU), Linz, Austria.


• Cancino Chacón, C. and Grachten, M. (2018).
  A Computational Study of the Role of Tonal Tension in Expressive Piano Performance.
  15th International Conference on Music Perception and Cognition (ICMPC 2018) & 10th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM 2018), Sydney/Graz/Montreal/La Plata.


• Cancino Chacón, C., Grachten, M., Sears, D. and Widmer, G. (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.


• Cancino Chacón, C., Gadermaier, T., Widmer, G. and Grachten, M. (2017).
  An Evaluation of Linear and Non-linear Models of Expressive Dynamics in Classical Piano and Symphonic Music.
  Machine Learning 106(6), 887-909. DOI: 10.1007/s10994-017-5631-y


• Grachten, M., Cancino Chacón, C., Gadermaier, T. and Widmer, G. (2017).
  Towards Computer-assisted Understanding of Expressive Dynamics in Symphonic Music.
  IEEE Multimedia 24 (1), pp. 36-46.


• Gadermaier, T., Grachten, M. and Cancino Chacón, C. (2016).
  Basis Function Modeling of Loudness Variations in Ensemble Performance.
  In Proceedings of the 2nd International Conference on New Music Concepts (ICNMC 2016), Treviso, Italy.

• Aljanaki, A. and Soleymani, M. (2018).
  A Data-driven Approach to Mid-level Perceptual Feature Modeling.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Aljanaki, A. and Widmer, G. (2018).
  Modeling Majorness as a Perceptual Property of Music.
  15th International Conference on Music Perception and Cognition (ICMPC 2018) & 10th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM 2018), Sydney/Graz/Montreal/La Plata. (to appear)


• Sears, D. and Widmer, G. (2018).
  Psychological Constraints on String-based Methods for Pattern Discovery in Polyphonic Corpora.
  15th International Conference on Music Perception and Cognition (ICMPC 2018) & 10th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM 2018), Sydney/Graz/Montreal/La Plata. (to appear)


• Lattner, S., Grachten, M. and Widmer, G. (2018).
  A Predictive Model for Music Based on Learned Relative Pitch Representations.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Cancino Chacón, C., Grachten, M. and Agres, K. (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.


• Nikrang, A., Sears, D. and Widmer, G. (2017).
  Automatic Estimation of Harmonic Tension by Distributed Representation of Chords.
  In Proceedings of the 13th International Symposium on Computer Music Multidisciplinary Research (CMMR 2017), Porto, Portugal.

• Korzeniowski, F. (2018).
  Harmonic Analysis of Musical Audio Using Deep Neural Networks.
  Ph.D. Thesis, Dept. of Computational Perception, Johannes Kepler University (JKU), Linz, Austria.


• Lattner, S., Grachten, M. and Widmer, G. (2018).
  Imposing Higher-level Structure in Polyphonic Music Generation Using Convolutional Restricted Boltzmann Machines and Constraints.
  Journal of Creative Music Systems 2(2), March 2018.


• Sears, D., Korzeniowski, F. and Widmer, G. (2018).
  Evaluating Language Models of Tonal Harmony.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Korzeniowski, F. and Widmer, G. (2018).
  Improved Chord Recognition by Combining Duration and Harmonic Language Models.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Korzeniowski, F. and Widmer, G. (2018).
  Genre-agnostic Key Classification with Convolutional Neural Networks.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Lattner, S., Grachten, M. and Widmer, G. (2018).
  Learning Transposition-Invariant Interval Features from Symbolic Music and Audio.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Korzeniowski, F. and Widmer, G. (2ß18).
  Automatic Chord Recognition with Higher-Order Harmonic Language Modelling.
  In Proceedings of the 26th European Signal Processing Conference (EUSIPCO 2018), Rome, Italy.


• Korzeniowski, F., Sears, D. and Widmer, G. (2018).
  A Large-scale Study of Language Models for Chord Prediction.
  In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, Canada.


• Lattner, S., Grachten, M. and Widmer, G. (2017).
  Learning Musical Relations using Gated Autoencoders.
  In Proceedings of the 2nd Conference on Computer Simulation of Musical Creativity (CSMC 2017), Milton Keynes, U.K.


• Sears, D., Arzt, A., Frostel, H., Sonnleitner, R. and Widmer, G. (2017).
  Modeling Harmony with Skip-Grams.
  In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China.


• Korzeniowski, F. and Widmer, G. (2017).
  On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition.
  In Proceedings of the 2017 AES International Conference on Semantic Audio, Erlangen, Germany. Audio Engineering Society (AES).


• Korzeniowski, F. and Widmer, G. (2017).
  End-to-End Musical Key Estimation Using a Convolutional Neural Network.
  In Proceedings of the 25th European Signal Processing Conference (EUSIPCO 2017), Kos, Greece.


• Korzeniowski, F. and Widmer, G. (2016).
  A Fully Convolutional Deep Auditory Model for Musical Chord Recognition.
  In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietro sul Mare, Italy.


• Korzeniowski, F. and Widmer, G. (2016).
  Feature Learning for Chord Recognition: The Deep Chroma Extractor.
  In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

• Bishop, L., Cancino Chacón, C. and Goebl, W. (2019).
  Eye Gaze as a Means of Giving and Seeking Information During Musical Interaction.
  Consciousness and Cognition 68 (2919), 73-96.


• Dorfer, M., Henkel, F. and Widmer, G. (2018).
  Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Henkel, F. (2018).
  A Regularization Study for Policy Gradient Methods.
  Masters Thesis, Dept. of Computational Perception, Johannes Kepler University Linz.


• Schauer, H. (2017).
  Automatic Music Accompaniment via On-line Time Warping and Kalman Filters.
  Masters Thesis, Dept. of Computational Perception, Johannes Kepler University Linz.


• Arzt, A. and Dorfer, M. (2017).
  Aktuelle Entwicklungen in der automatischen Musikverfolgung.
  In DGI Workshop: Musik trifft Informatik; 47. Jahrestagung der Gesellschaft für Informatik (GI), Chemnitz, Germany.


• Arzt, A. (2016).
  Flexible and Robust Music Tracking.
  Ph.D. Thesis, Dept. of Computational Perception, Johannes Kepler University Linz.
  Complete bibliographic metadata here.


• Dorfer, M., Arzt, A., Böck, S., Durand, A. and Widmer, G. (2016).
  Live Score Following on Sheet Music Images.
  17th International Society for Music Information Retrieval Conference (ISMIR 2016), Late Breaking/Demo Papers, New York, NY.


• Dorfer, M., Arzt, A. and Widmer, G. (2016).
  Towards Score Following in Sheet Music Images.
  In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

• Kelz, R., Böck, S., and Widmer, G. (2019).
  Multitask Learning for Polyphonic Piano Transcription, a Case Study.
  In Proceedings of the First International Workshop on Multilayer Music Representation and Processing (MMRP19), Milano, Italy.


• Dorfer, M. (2018).
  Multimodal Deep Representation Learning and its Application to Audio and Sheet Music.
  Ph.D. Thesis, Dept. of Computational Perception, Johannes Kepler University (JKU), Linz, Austria.


• Arzt, A. and Lattner, S. (2018).
  Audio-to-Score Alignment using Transposition-invariant Features.
  In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.


• Kelz, R. and Widmer, G. (2018).
  Learning to Transcribe by Ear.
  arXiv pre-print.


• Dorfer, M., Hajic, J., Arzt, A., Frostel, H. and Widmer, G. (2018).
  Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification.
  Transactions of the International Society for Music Information Retrieval (TISMIR) 1(1), 22-33.
  DOI (with supplementary material): http://doi.org/10.5334/tismir.12


• Dorfer, M., Schlüter, J., Vall, A., Korzeniowski, F., and Widmer, G. (2018).
  End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss.
  International Journal on Multimedia Information Retrieval.
  DOI (with supplementary material): https://doi.org/10.1007/s13735-018-0151-5
  


• Vogl, R., Widmer, G. and Knees, P. (2018).
  Towards Multi-Instrument Drum Transcription.
  In Proceedings of the 2018 International Conference on Digital Audio Effects (DAFx 2018), Aveiro, Portugal.


• Müller, M., Arzt, A., Balke, S., Dorfer, M., and Widmer, G. (2019).
  Cross-Modal Music Retrieval and Synchronization: An Overview of Key Methodologies.
  IEEE Signal Processing Magazine 36(1), 52-62.


• Dorfer, M., Hajic, J. and Widmer, G. (2018).
  Attention as a Perspective for Learning Tempo-invariant Audio Queries.
  In Proceedings of the Joint ICML, IJCAI/ECAI, AAMAS Workshop on Machine Learning for Music, Stockholm, Sweden.


• Kelz, R. and Widmer, G. (2018).
  Investigating Label Noise Sensitivity of Convolutional Neural Networks for Fine-grained Audio Signal Labelling.
  In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, Canada.


• Arzt, A. and Widmer, G. (2017).
  Piece Identification in Classical Piano Music Without Reference Scores.
  In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China.


• Dorfer, M., Arzt, A. and Widmer, G. (2017).
  Learning Audio-Sheet Music Correspondences for Score Identification and Offline Alignment.
  In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China.


• Kelz, R. and Widmer, G. (2017).
  An Experimental Analysis of the Entanglement Problem in Neural-Network-based Music Transcription Systems.
  In Proceedings of the 2017 AES International Conference on Semantic Audio, Erlangen, Germany. Audio Engineering Society (AES).


• Dorfer, M., Arzt, A. and Widmer, G. (2016).
  Towards End-to-end Audio-Sheet-Music Retrieval.
  In NIPS 2016 End-to-end Learning for Speech and Audio Processing Workshop, Barcelona, Spain.


• Kelz, R., Dorfer, M., Korzeniowski, F., Böck, S., Arzt, A. and Widmer, G. (2016).
  On the Potential of Simple Framewise Approaches to Piano Transcription.
  In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

• A. Automatic Accompaniment:
  
  
• Cancino Chacón, C., Bonev, M., Durand, A., Grachten, M., Arzt, A., Bishop, L., Goebl, W. and Widmer, G. (2017).
  The Accompanion v0.1: An Expressive Accompaniment System.
  Late breaking / demo papers, 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China.
  Demo Videos on a Bösendorfer CEUS: Mozart Sonata K.545, 2nd mvt. (Werner Goebl); "The Wild Geese" (Gerhard Widmer)


• B. Music Search and Recommendation:
  
  
• Vall, A. and Widmer, G. (2018).
  Machine Learning Approaches to Hybrid Music Recommender Systems.
  In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2018), Dublin, Ireland.


• Vall, A., Quadrana, M., Schedl, M. and Widmer. G. (2018).
  The Importance of Song Context and Song Order in Automated Music Playlist Generation.
  15th International Conference on Music Perception and Cognition (ICMPC 2018) & 10th Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM 2018), Sydney/Graz/Montreal/La Plata. (to appear)


• Vall, A., Dorfer, M., Schedl, M. and Widmer, G. (2018).
  A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership.
  In Proceedings of the Symposium on Applied Computing (SAC 2018), Pau, France.

This project receives funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No 670035.