This is the demo page for the paper 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 contains transcriptions of the electric guitar performance of 240 tablatures rendered with different tones. Moreover, we benchmark theperformance 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 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.
Training Data Sample
Audio are sampled from training split with different timbre in each column.
| DI | Marshall | Fender-twins | Mesa | |
| 1. | ||||
| 2. |
Realistic Data Transcription
| Source | Proposed model |
Dataset
Contact
Yu-Hua Chen f08946011@ntu.edu.tw