towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model
Abstract
In this paper, we propose a new dataset named EGDB, that con-tains transcriptions of the electric guitar performance of 240 tab-latures rendered with different tones. Moreover, we benchmark theperformance of two well-known transcription models proposed orig-inally for the piano on this dataset, along with a multi-loss Trans-former model that we newly propose. Our evaluation on this datasetand a separate set of real-world recordings demonstrate the influenceof timbre on the accuracy of guitar sheet transcription, the potentialof using multiple losses for Transformers, as well as the room forfurther improvement for this task.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2022
- DOI:
- 10.48550/arXiv.2202.09907
- arXiv:
- arXiv:2202.09907
- Bibcode:
- 2022arXiv220209907C
- Keywords:
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- Computer Science - Sound;
- Electrical Engineering and Systems Science - Audio and Speech Processing
- E-Print:
- to be published at ICASSP 2022