Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70552-6_2
Reference39 articles.
1. Lecture Notes in Computer Science;M Alfaro-Contreras,2019
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3. Arroyo, V., Valero-Mas, J.J., Calvo-Zaragoza, J., Pertusa, A.: neural audio-to-score music transcription for unconstrained polyphony using compact output representations. In: Proceedings of the 47th IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4603–4607. Singapore, Singapore (2022)
4. Baró, A., Riba, P., Calvo-Zaragoza, J., Fornés, A.: From optical music recognition to handwritten music recognition: a baseline. Pattern Recogn. Lett. 123, 1–8 (2019)
5. Baró, A., Riba, P., Fornés, A.: Musigraph: Optical music recognition through object detection and graph neural network. In: International Conference on Frontiers in Handwriting Recognition, pp. 171–184. Springer (2022). https://doi.org/10.1007/978-3-031-21648-0_12
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