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- W4287066702 abstract "Audio-to-score alignment (A2SA) is a multimodal task consisting in the alignment of audio signals to music scores. Recent literature confirms the benefits of Automatic Music Transcription (AMT) for A2SA at the frame-level. In this work, we aim to elaborate on the exploitation of AMT Deep Learning (DL) models for achieving alignment at the note-level. We propose a method which benefits from HMM-based score-to-score alignment and AMT, showing a remarkable advancement beyond the state-of-the-art. We design a systematic procedure to take advantage of large datasets which do not offer an aligned score. Finally, we perform a thorough comparison and extensive tests on multiple datasets." @default.
- W4287066702 created "2022-07-25" @default.
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- W4287066702 date "2021-07-27" @default.
- W4287066702 modified "2023-09-28" @default.
- W4287066702 title "Audio-to-Score Alignment Using Deep Automatic Music Transcription" @default.
- W4287066702 doi "https://doi.org/10.48550/arxiv.2107.12854" @default.
- W4287066702 hasPublicationYear "2021" @default.
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