Widmer, G. (2017). 
Getting Closer to the Essence of Music: The Con Espressione Manifesto, opens an external URL in a new window.
ACM Transactions on Intelligent Systems and Technology 8(2), Article 19, Jan. 2017. DOI: 10.1145/2899004, opens an external URL in a new window 

Link to final published version via ACM AuthorIzer:
Getting Closer to the Essence of Music: The Con Espressione Manifesto, opens an external URL in a new window

Materials (slides, opens an external URL in a new window, data & software, opens an external URL in a new window) from the Tutorial on Computational Modeling of Expressive Performance, opens an external URL in a new window given by Carlos Cancino, Katerina Kosta, and Maarten Grachten at the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands (Nov. 2019).

Chowdhury, S. and Widmer, G. (2021).
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation, opens an external URL in a new window.
In Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021), Toronto, Canada.

Cancino Chacón, C., Peter, S., Karystinaios, E. and Widmer, G. (2021).
Towards Quantifying Differences in Expressive PianoPerformances: Are Euclidean-like Distance Measures Enough?
in 18th Rhythm Production and Perception Workshop (RPPW 2021), Oslo, Norway.

Cancino Chacón, C., Peter, S., Chowdhury, S., Aljanaki, A. and Widmer, G. (2021).
Sorting Musical Expression: Characterization of Descriptions of Expressive Piano Performances.
In 16th International Conference on Music Perception and Cognition ICMPC 2021) and 11th Triennial Conference of ESCOM (ICMPC-ESCOM 2021).

Cancino Chacón, C., Aljanaki, A., Chowdhury, S., Peter, S. and Widmer, G. (2020). [Data, opens an external URL in a new window | Video, opens an external URL in a new window]
On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game, opens an external URL in a new window.
In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), Montreal, Canada.

Shi, Z., Cancino Chacón, C., and Widmer, G. (2019).
User Curated Shaping of Expressive Performances, opens an external URL in a new window.
In ICML 2019 Workshop on Machine Learning for Music Discovery, 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA.

Gadermaier, T. and Widmer, G. (2019). [Data, opens an external URL in a new window]
A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music, opens an external URL in a new window.
In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

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

Cancino Chacón, C. (2018). [Code, opens an external URL in a new window]
Computational Modeling of Expressive Music Performance through Linear and Non-linear Basis Function Models, opens an external URL in a new window.
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, opens an external URL in a new window.
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). [Code, opens an external URL in a new window]
What Were You Expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music, opens an external URL in a new window.
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, opens an external URL in a new window.
Machine Learning 106(6), 887-909. DOI: 10.1007/s10994-017-5631-y, opens an external URL in a new window

Grachten, M., Cancino Chacón, C., Gadermaier, T. and Widmer, G. (2017).
Towards Computer-assisted Understanding of Expressive Dynamics in Symphonic Music, opens an external URL in a new window.
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, opens an external URL in a new window.
In Proceedings of the 2nd International Conference on New Music Concepts (ICNMC 2016), Treviso, Italy.

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.

Chowdhury, S. and Widmer, G. (2021).
On Perceived Emotion in Expressive Piano Performance: Further Experimental Evidence for the Relevance of Mid-level Perceptual Features, opens an external URL in a new window.
In Proceedings of the 22nd International Society for Music Information Retrieval Conference (ISMIR 2021), [online conference].

Chowdhury, S., Praher, V. and Widmer, G. (2021). [Code, opens an external URL in a new window]
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities, opens an external URL in a new window.
In Proceedings of the 18th Sound and Music Computing Conference (SMC2021), Virtual Conference, June 29–July 01, 2021

Haunschmid, V., Manilow, E. and Widmer, G. (2020). [Code, opens an external URL in a new window]
audioLIME: Listenable Explanations Using Source Separation, opens an external URL in a new window
In Proceedings of the 13th International Workshop on Machine Learning and Music (MML 2020), ECML-PKDD 2020.

Koutini, K., Chowdhury, S., Haunschmid, V., Eghbal-zadeh, H., and Widmer, G. (2019). [Code, opens an external URL in a new window]
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs, opens an external URL in a new window.
In Proceedings of the MediaEval 2019 Multimedia Benchmark Workshop, Sophia Antipolis, France.
(Describes the approach developed to obtain 1st Place in the "Emotion and Themes Recognition in Music" Task in the MediaEval-2019 Challenge.)

Chowdhury, S., Haunschmid, V. and Widmer, G. (2019). [Code, opens an external URL in a new window]
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features, opens an external URL in a new window.
In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

Haunschmid, V., Chowdhury, S. and Widmer, G. (2019). [Code, opens an external URL in a new window]
Two-level Explanations in Music Emotion Recognition, opens an external URL in a new window.
In ICML 2019 Workshop on Machine Learning for Music Discovery, 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA.

Aljanaki, A. and Soleymani, M. (2018). [Data, opens an external URL in a new window]
A Data-driven Approach to Mid-level Perceptual Feature Modeling, opens an external URL in a new window.
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, opens an external URL in a new window.
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.

Sears, D. and Widmer, G. (2018).
Psychological Constraints on String-based Methods for Pattern Discovery in Polyphonic Corpora, opens an external URL in a new window.
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.

Lattner, S., Grachten, M. and Widmer, G. (2018).
A Predictive Model for Music Based on Learned Relative Pitch Representations, opens an external URL in a new window.
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, opens an external URL in a new window.
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, opens an external URL in a new window.
In Proceedings of the 13th International Symposium on Computer Music Multidisciplinary Research (CMMR 2017), Porto, Portugal.

Sears, D. and Widmer, G. (2020).
Beneath (or Beyond) the Surface: Discovering Voice-leading Patterns with Skip-grams, opens an external URL in a new window. Journal of Mathematics and Music (in press). DOI: 10.1080/17459737.2020.1785568, opens an external URL in a new window.

Lattner, S. (2019).
Modeling Musical Structure with Artificial Neural Networks, opens an external URL in a new window.
Ph.D. Thesis, Institute of Computational Perception, Johannes Kepler University (JKU), Linz, Austria.

Simonetta, F., Cancino-Chacon, C., Ntalampiras, S. and Widmer, G. (2019). [Code, opens an external URL in a new window | Data, opens an external URL in a new window]
A Convolutional Approach to Melody Line Identification in Symbolic Scores, opens an external URL in a new window.
In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

Korzeniowski, F. (2018).
Harmonic Analysis of Musical Audio Using Deep Neural Networks, opens an external URL in a new window.
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, opens an external URL in a new window.
Journal of Creative Music Systems 2(2), March 2018.

Sears, D., Korzeniowski, F. and Widmer, G. (2018).
Evaluating Language Models of Tonal Harmony, opens an external URL in a new window.
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, opens an external URL in a new window.
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, opens an external URL in a new window.
In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.

Lattner, S., Grachten, M. and Widmer, G. (2018). [Code, opens an external URL in a new window]
Learning Transposition-Invariant Interval Features from Symbolic Music and Audio, opens an external URL in a new window.
In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France.

Korzeniowski, F. and Widmer, G. (2018).
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling, opens an external URL in a new window.
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, opens an external URL in a new window.
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., opens an external URL in a new window
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, opens an external URL in a new window.
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, opens an external URL in a new window.
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, opens an external URL in a new window.
In Proceedings of the 25th European Signal Processing Conference (EUSIPCO 2017), Kos, Greece.

Korzeniowski, F. and Widmer, G. (2016). [Code, opens an external URL in a new window]
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition, opens an external URL in a new window.
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). [Code, opens an external URL in a new window]
Feature Learning for Chord Recognition: The Deep Chroma Extractor, opens an external URL in a new window.
In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

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.

Henkel, F. and Widmer, G. (2021). [Code, opens an external URL in a new window]
Real-time Music Following in Score Sheet Images via Multi-resolution Prediction, opens an external URL in a new window.
Frontiers in Computer Science (Computer Vision) 3:718340.

Henkel, F., Schwaiger, S. and Widmer, G. (2021). [Code, opens an external URL in a new window]
Fully Automated Page Turning on Real Scores, opens an external URL in a new window.
ISMIR 2021 Late Breaking/Demo, 22nd International Society for Music Information Retrieval Conference (ISMIR 2021), online conference.

Henkel, F. and Widmer, G. (2021). [Code, opens an external URL in a new window]
Multi-modal Conditional Bounding Box Regression for Music Score Following, opens an external URL in a new window.
In Proceedings of the 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland, 2021.

Bishop, L., Cancino-Chacón, C., & Goebl, W. (2021).
Beyond Synchronization: Body Gestures and Gaze Direction in Duo Performance, opens an external URL in a new window.
In Timmers, R., Bailes, F., and Daffern, H. (Eds.), Together in Music: Participation, Co-Ordination, and Creativity in Ensembles. Oxford: Oxford University Press.

Henkel, F., Kelz, R. and Widmer, G. (2020). [Code, opens an external URL in a new window | Data, opens an external URL in a new window | Video]
Learning to Read and Follow Music in Complete Score Sheet Images, opens an external URL in a new window.
In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), Montreal, Canada.

Lartillot, O., Cancino-Chacón, C. and Brazier, C. (2020:
Real-time Visualization of Fugue Played by a String Quartet, opens an external URL in a new window.
In Proceedings of the 17th Sound and Music Computing Conference (SMC 2020), Torino, Italy.

Bishop, L., Cancino-Chacón, C. and Goebl, W. (2019).
Moving to Communicate, Moving to Interact: Patterns of Body Motion in Musical Duo Performance, opens an external URL in a new window.
Music Perception 37(1), s 1- 25 .
DOI: https://doi.org/10.1525/mp.2019.37.1.1, opens an external URL in a new window

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

Weigl, D., Cancino Chacón, C., Bonev, M. and Goebl, W. (2019).
Linking and Visualising Performance Data and Semantic Music Encodings in Real Time.
Late breaking / demo papers, 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

Henkel, F., Kelz, R. and Widmer, G. (2019). [Code, opens an external URL in a new window | Data, opens an external URL in a new window]
Audio-conditioned U-Net for Position Estimation in Full Sheet Images, opens an external URL in a new window.
In International Workshop on Reading Music Systems (WoRMS 2019), Delft, The Netherlands.

Henkel, F., Balke, S., Dorfer, M. and Widmer, G. (2019). [Code, opens an external URL in a new window | Data, opens an external URL in a new window]
Score Following as a Multi-modal Reinforcement Learning Problem, opens an external URL in a new window.
Transactions of the International Society for Music Information Retrieval 2(1), 67-81.

Dorfer, M., Henkel, F. and Widmer, G. (2018). [Code, opens an external URL in a new window | Data, opens an external URL in a new window]
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game, opens an external URL in a new window.
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, opens an external URL in a new window.
Masters Thesis, Dept. of Computational Perception, Johannes Kepler University Linz.

Schauer, H. (2017).
Automatic Music Accompaniment via On-line Time Warping and Kalman Filters, opens an external URL in a new window.
Masters Thesis, Dept. of Computational Perception, Johannes Kepler University Linz.

Arzt, A. and Dorfer, M. (2017).
Aktuelle Entwicklungen in der automatischen Musikverfolgung, opens an external URL in a new window.
In DGI Workshop: Musik trifft Informatik; 47. Jahrestagung der Gesellschaft für Informatik (GI), Chemnitz, Germany.

Arzt, A. (2016).
Flexible and Robust Music Tracking, opens an external URL in a new window.
Ph.D. Thesis, Dept. of Computational Perception, Johannes Kepler University Linz.
Complete bibliographic metadata here, opens an external URL in a new window.

Dorfer, M., Arzt, A., Böck, S., Durand, A. and Widmer, G. (2016).
Live Score Following on Sheet Music Images, opens an external URL in a new window.
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, opens an external URL in a new window.
In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

Eghbal-zadeh, H., Henkel, F. and Widmer, G. (2021).
Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning. In Proceedings of the ICLR Workshop on Self-supervision for Reinforcement Learning (SSL-RL), Ninth International Conference on Learning Representations (ICLR 2021).

Balke, S., Dorfer, M., Carvalho, L., Arzt, A. and Widmer, G. (2019). [Code, opens an external URL in a new window | Data, opens an external URL in a new window]
Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval, opens an external URL in a new window.
In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

Kelz, R. and Widmer, G. (2019). [Code, opens an external URL in a new window]
Towards Interpretable Polyphonic Transcription with Invertible Neural Networks, opens an external URL in a new window.
In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

Grachten, M., Cancino Chacón, and Gadermaier, T. (2019). [Code, opens an external URL in a new window]
partitura: a Python Package for Handling Symbolic Musical Data, opens an external URL in a new window.
Late breaking / demo papers, 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

Kelz, R., Böck, S. and Widmer, G. (2019). [Code, opens an external URL in a new window]
Deep Polyphonic ADSR Piano Note Transcription, opens an external URL in a new window.
In Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), Brighton, UK.

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

Müller, M., Arzt, A., Balke, S., Dorfer, M., and Widmer, G. (2019).
Cross-Modal Music Retrieval and Synchronization: An Overview of Key Methodologies., opens an external URL in a new window
IEEE Signal Processing Magazine 36(1), 52-62.

Dorfer, M. (2018).
Multimodal Deep Representation Learning and its Application to Audio and Sheet Music, opens an external URL in a new window.
Ph.D. Thesis, Dept. of Computational Perception, Johannes Kepler University (JKU), Linz, Austria.

Arzt, A. and Lattner, S. (2018). [Code, opens an external URL in a new window]
Audio-to-Score Alignment using Transposition-invariant Features, opens an external URL in a new window.
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, opens an external URL in a new window.
arXiv pre-print.

Dorfer, M., Hajic, J., Arzt, A., Frostel, H. and Widmer, G. (2018). [Code, opens an external URL in a new window | Data, opens an external URL in a new window]
Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification, opens an external URL in a new window.
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, opens an external URL in a new window

Dorfer, M., Schlüter, J., Vall, A., Korzeniowski, F., and Widmer, G. (2018). [Code, opens an external URL in a new window]
End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss. , opens an external URL in a new window
International Journal on Multimedia Information Retrieval.
DOI (with supplementary material): https://doi.org/10.1007/s13735-018-0151-5, opens an external URL in a new window

Vogl, R., Widmer, G. and Knees, P. (2018).
Towards Multi-Instrument Drum Transcription, opens an external URL in a new window.
In Proceedings of the 2018 International Conference on Digital Audio Effects (DAFx 2018), Aveiro, Portugal.

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

Kelz, R. and Widmer, G. (2018). [Code, opens an external URL in a new window]
Investigating Label Noise Sensitivity of Convolutional Neural Networks for Fine-grained Audio Signal Labelling, opens an external URL in a new window.
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, opens an external URL in a new window.
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, opens an external URL in a new window.
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, opens an external URL in a new window.
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, opens an external URL in a new window.
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). [Code, opens an external URL in a new window]
On the Potential of Simple Framewise Approaches to Piano Transcription, opens an external URL in a new window.
In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York, NY.

A. Automatic Accompaniment and Collaborative Expressive Performance:

Cancino Chacón, C., Balke, S., Henkel, F., Stussak, C. and Widmer, G. (2019). [Code, opens an external URL in a new window]
The Con Espressione! Exhibit: Exploring Human-Machine Collaboration in Expressive Performance, opens an external URL in a new window.
Late breaking / demo papers, 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands.

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, opens an external URL in a new window.
Late breaking / demo papers, 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China.

Demo Videos (2019) with piano music for four hands:

• Carlos Cancino, opens an external URL in a new window and the ACCompanion play a Ravel Minuet, opens an external URL in a new window
• Werner Goebl, opens an external URL in a new window and the ACCompanion with a Brahms Waltz, opens an external URL in a new window

B. Expressive Micro-Timing:

Peter, S.D. and Widmer, G. (2019).
A Sequencer with Decoupled Track Timing, opens an external URL in a new window. (Demonstration Paper)
In Proceedings of the 16th Sound & Music Computing Conference (SMC 2019), Malaga, Spain.

C. Music Search and Recommendation:

Vall, A., Dorfer, M., Eghbal-zadeh, H., Schedl, M., Burjorjee, K. and Widmer, G. (2019). [Code, opens an external URL in a new window]
Feature-combination Hybrid Recommender Systems for Automated Music Playlist Continuation, opens an external URL in a new window.
User Modeling and User-Adapted Interaction (2019), 1-46. DOI: 10.1007/s11257-018-9215-8, opens an external URL in a new window

Vall, A. and Widmer, G. (2018).
Machine Learning Approaches to Hybrid Music Recommender Systems, opens an external URL in a new window.
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. (2018).
Machine Learning Approaches to Hybrid Music Recommender Systems, opens an external URL in a new window.
Ph.D. Thesis, Johannes Kepler University Linz, Austria.

Vall, A., Quadrana, M., Schedl, M. and Widmer. G. (2018).
The Importance of Song Context and Song Order in Automated Music Playlist Generation, opens an external URL in a new window.
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.

Vall, A., Dorfer, M., Schedl, M. and Widmer, G. (2018).
A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership, opens an external URL in a new window.
In Proceedings of the Symposium on Applied Computing (SAC 2018), Pau, France.