A new popular music audio-to-score dataset and a pre-trained-feature model with augmentation cut symbol error rate on classical quartets from 15.3% to 4.98% and establish a 20.92% SER benchmark for lead sheets.
Valero-Mas, and Jorge Calvo- Zaragoza
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Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset
A new popular music audio-to-score dataset and a pre-trained-feature model with augmentation cut symbol error rate on classical quartets from 15.3% to 4.98% and establish a 20.92% SER benchmark for lead sheets.