2020W

• Applied Deep Learning / VU / 194.077

• Optical Music Recognition with Deep Learning
  2017 – 2020 / McGill University

• Self-Learning Optical Music Recognition / PhD Thesis by A. Pacha / Supervisor, Reviewer: H. Eidenberger, I. Fujinaga, O. Marques; Institute of Information Systems Engineering, 2019; oral examination: 2019-07-04
• Introduction to the MUSCIMA++ Dataset / J. Hajič, A. Pacha / Publication type: YouTube Video, project: Deep Learning for Optical Music Recognition; 2019
• Identification and Cross-Document Alignment of Measures in Music Score Images / S. Waloschek, A. Pacha, A. Hadjakos / Talk: 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands; 2019-11-04 - 2019-11-08; in: "20th International Society for Music Information Retrieval Conference", (2019), 137 - 143
• Incremental Supervised Staff Detection / A. Pacha / Talk: 2nd International Workshop on Reading Music Systems, Delft, The Netherlands; 2019-11-02; in: "Proceedings of the 2nd International Workshop on Reading Music Systems", (2019), 16 - 20
• Learning Notation Graph Construction for Full-Pipeline Optical Music Recognition / A. Pacha, J. Calvo-Zaragoza, J. Hajič / Talk: 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands; 2019-11-04 - 2019-11-08; in: "20th International Society for Music Information Retrieval Conference", (2019), 75 - 82
• Optical Music Recognition for Dummies / J. Calvo-Zaragoza, J. Hajič, A. Pacha, I. Fujinaga / Keynote Lecture: 19th International Society for Music Information Retrieval Conference, Paris, France; 2018-09-23 - 2018-09-27
• A Baseline for General Music Object Detection with Deep Learning / A. Pacha, J. Hajič, J. Calvo-Zaragoza / Applied Sciences, 8 (2018), 9; 1488 - 1508
• Discussion Group Summary: Optical Music Recognition / J. Calvo-Zaragoza, J. Hajič, A. Pacha / in: "Graphics Recognition. Current Trends and Evolutions", A. Fornés, B. Lamiroy (ed.); Springer International Publishing, Cham, 2018, (invited), ISBN: 978-3-030-02284-6, 152 - 157
• Handwritten Music Object Detection: Open Issues and Baseline Results / A. Pacha, K. Choi, B. Coüasnon, Y. Ricquebourg, H. Eidenberger / Talk: 2018 13th IAPR Workshop on Document Analysis Systems (DAS), Wien; 2018-04; in: "2018 13th IAPR Workshop on Document Analysis Systems (DAS)", (2018), 6 pages
• Self-Learning Optical Music Recognition / A. Pacha / Talk: Vienna young Scientists Symposium, Wien; 2018-06-07 - 2018-06-08; in: "Vienna young Scientists Symposium", Book-of-Abstracts.com, (2018), ISBN: 978-3-9504017-8-3; 34 - 35
• Advancing OMR as a Community: Best Practices for Reproducible Research / A. Pacha / Talk: 1st International Workshop on Reading Music Systems, Paris, France; 2018-09-20; in: "Proceedings of the 1st International Workshop on Reading Music Systems", (2018), 19 - 20
• How Current Optical Music Recognition Systems Are Becoming Useful for Digital Libraries / J. Hajič, M. Kolárová, A. Pacha, J. Calvo-Zaragoza / Talk: 5th International Conference on Digital Libraries for Musicology, Paris, France; 2018-09-28; in: "Proceedings of the 5th International Conference on Digital Libraries for Musicology", (2018), 57 - 61
• Optical Music Recognition in Mensural Notation with Region-Based Convolutional Neural Networks / A. Pacha, J. Calvo-Zaragoza / Talk: 19th International Society for Music Information Retrieval Conference, Paris, France; 2018-09-23 - 2018-09-27; in: "Proceedings of the 19th International Society for Music Information Retrieval Conference", (2018), 240 - 247
• Towards a Universal Music Symbol Classifier / A. Pacha, H. Eidenberger / Talk: International Workshop on Graphics Recognition, New York; 2017-11-09 - 2017-11-10; in: "Proceedings of the 12th International Workshop on Graphics Recognition", (2017), ISBN: 978-1-5386-3586-5; 2 pages
• Towards Self-Learning Optical Music Recognition / A. Pacha, H. Eidenberger / Talk: 16th IEEE International Conference on Machine Learning and Applications, Cancun; 2017-12-18 - 2017-12-21; in: "2017 16th IEEE International Conference on Machine Learning and Applications", (2017), 6 pages
• Quality Assurance for Data from Low-Tech Participants in Distributed Automation Engineering Environments / R. Mordinyi, A. Pacha, S. Biffl / Talk: 16th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2011), Toulouse, France; 2011-09-05 - 2011-09-09; in: "Proceeding of 16th IEEE International Conference on Emerging Technologies and Factory Automation", Z. Mammeri (ed.); (2011), ISBN: 978-1-4577-0018-7; 1 - 4