YU-HUA CHENHsiao, Wen YiWen YiHsiaoHsieh, Tsu KuangTsu KuangHsiehJang, Jyh Shing RogerJyh Shing RogerJangYI-HSUAN YANG2023-10-062023-10-062022-01-01978166540540915206149https://scholars.lib.ntu.edu.tw/handle/123456789/635982In this paper, we propose a new dataset named EGDB, that contains transcriptions of the electric guitar performance of 240 tablatures rendered with different tones. Moreover, we benchmark the performance of two well-known transcription models proposed originally for the piano on this dataset, along with a multi-loss Transformer model that we newly propose. Our evaluation on this dataset and a separate set of real-world recordings demonstrate the influence of timbre on the accuracy of guitar sheet transcription, the potential of using multiple losses for Transformers, as well as the room for further improvement for this task.Dataset | guitar transcription | Transformerconference paper10.1109/ICASSP43922.2022.97476972-s2.0-85134025471https://api.elsevier.com/content/abstract/scopus_id/85134025471