2024W

• Applied Deep Learning / 194.077 / VU

• Optical Music Recognition with Deep Learning
  2017 – 2020 / Universidad de Alicante

• Proceedings of the 5th International Workshop on Reading Music Systems / Calvo-Zaragoza, J., Pacha, A., & Shatri, E. (Eds.). (2023). Proceedings of the 5th International Workshop on Reading Music Systems. https://doi.org/10.48550/arXiv.2311.04091
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• Understanding Optical Music Recognition / Calvo-Zaragoza, J., Hajič, J. jr., & Pacha, A. (2021). Understanding Optical Music Recognition. ACM Computing Surveys, 53(4), 1–35. https://doi.org/10.1145/3397499
• The DeepScoresV2 Dataset and Benchmark for Music Object Detection / Tuggener, L., Satyawan, Y. P., Pacha, A., Schmidhuber, J., & Stadelmann, T. (2021). The DeepScoresV2 Dataset and Benchmark for Music Object Detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9188–9195). Springer International Publishing. https://doi.org/10.1109/ICPR48806.2021.9412290
• The Challenge of Reconstructing Digits in Music Scores / Pacha, A. (2021). The Challenge of Reconstructing Digits in Music Scores. In J. Calvo-Zaragoza & A. Pacha (Eds.), Proceedings of the 3rd International Workshop on Reading Music Systems (pp. 4–7). Proceedings of the 3rd International Workshop on Reading Music Systems. http://hdl.handle.net/20.500.12708/58655
• Incremental Supervised Staff Detection / Pacha, A. (2019). Incremental Supervised Staff Detection. In Proceedings of the 2nd International Workshop on Reading Music Systems (pp. 16–20). http://hdl.handle.net/20.500.12708/57913
• Identification and Cross-Document Alignment of Measures in Music Score Images / Waloschek, S., Pacha, A., & Hadjakos, A. (2019). Identification and Cross-Document Alignment of Measures in Music Score Images. In 20th International Society for Music Information Retrieval Conference (pp. 137–143). http://hdl.handle.net/20.500.12708/57912
• Learning Notation Graph Construction for Full-Pipeline Optical Music Recognition / Pacha, A., Calvo-Zaragoza, J., & Hajič, J. jr. (2019). Learning Notation Graph Construction for Full-Pipeline Optical Music Recognition. In 20th International Society for Music Information Retrieval Conference (pp. 75–82). http://hdl.handle.net/20.500.12708/57911
• Self-learning optical music recognition / Pacha, A. (2019). Self-learning optical music recognition [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.68485
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• A Baseline for General Music Object Detection with Deep Learning / Pacha, A., Hajič, jr., Jan, & Calvo-Zaragoza, J. (2018). A Baseline for General Music Object Detection with Deep Learning. Applied Sciences, 8(9), 1–21. https://doi.org/10.3390/app8091488
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• Discussion Group Summary: Optical Music Recognition / Calvo-Zaragoza, J., Hajič, J., & Pacha, A. (2018). Discussion Group Summary: Optical Music Recognition. In A. Fornés & B. Lamiroy (Eds.), Graphics Recognition. Current Trends and Evolutions 12th IAPR International Workshop, GREC 2017, Kyoto, Japan, November 9-10, 2017, Revised Selected Papers (pp. 152–157). Springer International Publishing. https://doi.org/10.1007/978-3-030-02284-6_12
• Self-Learning Optical Music Recognition / Pacha, A. (2018). Self-Learning Optical Music Recognition. In Vienna young Scientists Symposium (pp. 34–35). Book-of-Abstracts.com. http://hdl.handle.net/20.500.12708/57433
• Optical Music Recognition for Dummies / Calvo-Zaragoza, J., Hajič, J. jr., Pacha, A., & Fujinaga, I. (2018). Optical Music Recognition for Dummies. 19th International Society for Music Information Retrieval Conference, Paris, France, EU. http://hdl.handle.net/20.500.12708/86918
• How current optical music recognition systems are becoming useful for digital libraries / Hajič, J. jr., Kolárová, M., Pacha, A., & Calvo-Zaragoza, J. (2018). How current optical music recognition systems are becoming useful for digital libraries. In Proceedings of the 5th International Conference on Digital Libraries for Musicology. 5th International Conference on Digital Libraries for Musicology, Paris, France, EU. https://doi.org/10.1145/3273024.3273034
• Optical Music Recognition in Mensural Notation with Region-Based Convolutional Neural Networks / Pacha, A., & Calvo-Zaragoza, J. (2018). Optical Music Recognition in Mensural Notation with Region-Based Convolutional Neural Networks. In Proceedings of the 19th International Society for Music Information Retrieval Conference (pp. 240–247). http://hdl.handle.net/20.500.12708/57436
• Advancing OMR as a Community: Best Practices for Reproducible Research / Pacha, A. (2018). Advancing OMR as a Community: Best Practices for Reproducible Research. In Proceedings of the 1st International Workshop on Reading Music Systems (pp. 19–20). http://hdl.handle.net/20.500.12708/57434
• Handwritten Music Object Detection: Open Issues and Baseline Results / Pacha, A., Choi, K.-Y., Coüasnon, B., Ricquebourg, Y., & Eidenberger, H. (2018). Handwritten Music Object Detection: Open Issues and Baseline Results. In 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). 2018 13th IAPR Workshop on Document Analysis Systems (DAS), Wien, Austria. https://doi.org/10.1109/das.2018.51
• Towards Self-Learning Optical Music Recognition / Pacha, A., & Eidenberger, H. (2017). Towards Self-Learning Optical Music Recognition. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 16th IEEE International Conference on Machine Learning and Applications, Cancun, Non-EU. https://doi.org/10.1109/icmla.2017.00-60
• Towards a Universal Music Symbol Classifier / Pacha, A., & Eidenberger, H. (2017). Towards a Universal Music Symbol Classifier. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). International Workshop on Graphics Recognition, New York, Non-EU. https://doi.org/10.1109/icdar.2017.265
• Quality assurance for data from low-tech participants in distributed automation engineering environments / Mordinyi, R., Pacha, A., & Biffl, S. (2011). Quality assurance for data from low-tech participants in distributed automation engineering environments. In Z. Mammeri (Ed.), ETFA2011. https://doi.org/10.1109/etfa.2011.6059149