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Institute of Computational Perception
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Whither Music?

Exploring Musical Possibilities via Machine Simulation

Scientific Publications

PERCEIVE AND PREDICT:
Explainable and Robust Perception & Prediction Models

Chowdhury, S. (2022).
Modeling Emotional Expression in Music Using Interpretable and Transferable Perceptual Features, opens an external URL in a new window.
PhD Thesis, Johannes Kepler University Linz.

Foscarin, F., Hoedt, K., Praher, V., Flexer, A. and Widmer, G. (2022). [Code, opens an external URL in a new window]
Concept-Based Techniques for "Musicologist-Friendly" Explanations in a Deep Music Classifier, opens an external URL in a new window.
In Proceedings of the 23rd International Society for Music Information Retrieval Conference (ISMIR 2022), Bengaluru, India. (to appear)

Karystinaios, E. and Widmer, G. (2022). [Code, opens an external URL in a new window]
Cadence Detection in Symbolic Classical Music using Graph Neural Networks, opens an external URL in a new window.
In Proceedings of the 23rd International Society for Music Information Retrieval Conference (ISMIR 2022), Bengaluru, India. (to appear)

Bjare, M., Lattner, S. and Widmer, G. (2022).
Differentiable Short-Term Models for Efficient Online Learning and Prediction in Monophonic Music, opens an external URL in a new window.
Transactions of the International Society for Music Information Retrieval 5(1), 190–207. DOI: http://doi.org/10.5334/tismir.123 ., opens an external URL in a new window

Martak, L.S., Kelz, R. and Widmer, G. (2022). [Code, opens an external URL in a new window]
Balancing Bias and Performance in Polyphonic Piano Transcription Systems, opens an external URL in a new window.
Frontiers in Signal Processing 2 (2022), DOI:10.3389/frsip.2022.975932   

Chowdhury, S. and Widmer, G. (2022). [Demo Video, opens an external URL in a new window]
Decoding and Visualising Intended Emotion in an Expressive Piano Performance.
Late-Breaking/Demo Papers, 23rd International Society for Music Information Retrieval Conference (ISMIR 2022), Bengaluru, India.

Hoedt, K., Praher, V., Flexer, A. and Widmer, G. (2022). [Code, opens an external URL in a new window]
Constructing Adversarial Examples to Investigate the Plausibility of Explanations in Deep Audio and Image Classifiers, opens an external URL in a new window.
Neural Computing and Applications (2022). DOI: 10.1007/s00521-022-07918-7, opens an external URL in a new window

Prinz, K., Flexer, A. and Widmer, G. (2022). [Code, opens an external URL in a new window]
Defending a Music Recommender Against Hubness-Based Adversarial Attacks, opens an external URL in a new window.
In Proceedings of the 2022 Sound and Music Computing (SMC 2022), Saint-Etienne, France.

Marták, L., Kelz, R. and Widmer, G. (2022).
Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-linear Modelling for Audio Source Separation, opens an external URL in a new window.
In Proceedings of the 24th International Congress on Acoustics (ICA 2022), Gyeongju, Korea.

PERFORM AND INTERACT:
Transparent Models of Musical Performance and Interaction

Cancino Chacon, C., Peter, S. and Widmer, G. (2022).
Can We Achieve Togetherness with an Artificial Partner? Insights and Challenges from Developing an Automatic Accompaniment System.
In Musical Togetherness Symposium (MTS-22), Vienna, Austria.

GENERATE:
Controllable Music Generation Models

Hausberger, A., Pichler, C., Pilkov, I., Wögerbauer, J., Cancino-Chacón, C. and Peter, S. (2022).
"On a Journey Together", opens an external URL in a new window: An interactively composed song, and our student team's submission to the AI Song Contest 2022, opens an external URL in a new window.

FUNDAMENTAL TECHNOLOGIES:
Musical Data, Representations, etc.

Cancino-Chacón, C., Peter, S., Karystinaios, E., Foscarin, F., Grachten, M. and Widmer, G. (2022). [Code, opens an external URL in a new window]
Partitura: A Python Package for Symbolic Music Processing, opens an external URL in a new window.
In Proceedings of the Music Encoding Conference (MEC 2022), Halifax, Canada.

Foscarin, F., Karystinaios, E., Peter, S., Cancino-Chacón, C., Grachten, M. and Widmer, G. (2022). [Doc, opens an external URL in a new window | Data, opens an external URL in a new window]
The match File Format: Encoding Alignments between Scores and Performances, opens an external URL in a new window.
In Proceedings of the Music Encoding Conference (MEC 2022), Halifax, Canada.

Henkel, F. (2022). [Code, opens an external URL in a new window]
Multi-modal Deep Learning for On-line Music Following in Score Sheet Images, opens an external URL in a new window.
PhD. Thesis, Inst. of Computational Perception, Johannes Kepler University Linz, Austria.

Acknowledgment

This project receives funding from the European Research Council (ERC), opens an external URL in a new window under the European Union's Horizon 2020 research and innovation programme under grant agreement No 101019375 (Whither Music?).