2022W

• Applied Deep Learning / 194.077 / VU

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

Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.

• 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 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
• The DeepScoresV2 Dataset and Benchmark for Music Object Detection / Tuggener, L., Satyawan, Y. P., Pacha, A., Schmidhuber, J., & Stadelmann, T. (2020). The DeepScoresV2 Dataset and Benchmark for Music Object Detection. In Lecture Notes in Computer Science. IAPR, Austria. Springer International Publishing. https://doi.org/10.1007/978-3-030-68799-1
• 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
• 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
• 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
• 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
• 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.), Lecture Notes in Computer Science (pp. 152–157). Springer International Publishing. https://doi.org/10.1007/978-3-030-02284-6_12
• 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
• 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
• 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