Multi-task Piano Transcription with Local Relative Time Attention
Automatic music transcription (AMT) is to transcribe music audios into musical symbol representations. Recently, the Transformer-based transcription systems have shown superiority on modeling note-wise sequences. For the frame-wise transcription targets in the AMT, the attention needs to focus more...
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| Vydané v: | Proceedings ... Asia-Pacific Signal and Information Processing Association Annual Summit and Conference APSIPA ASC ... (Online) s. 966 - 971 |
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| Hlavní autori: | , , , |
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IEEE
31.10.2023
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| ISSN: | 2640-0103 |
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| Abstract | Automatic music transcription (AMT) is to transcribe music audios into musical symbol representations. Recently, the Transformer-based transcription systems have shown superiority on modeling note-wise sequences. For the frame-wise transcription targets in the AMT, the attention needs to focus more on the neighboring frames instead of notes in context. In this work, we propose a multi-task transcription system with a self-attention mechanism. The designed relative positional self-attention aims to model frame-wise short-term dependencies in audio and transcribe music of variable length. Adding the learnable attention mask on multiple attention head, the network can obtain different multi-scale attention distances for each subtask. Experiments on the MAESTRO dataset show the proposed system with the local relative time attention mechanism achieves state-of-the-art transcription performance on both frame and note metrics (frame F1 93.40%, note with offset F1 88.50%). |
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| AbstractList | Automatic music transcription (AMT) is to transcribe music audios into musical symbol representations. Recently, the Transformer-based transcription systems have shown superiority on modeling note-wise sequences. For the frame-wise transcription targets in the AMT, the attention needs to focus more on the neighboring frames instead of notes in context. In this work, we propose a multi-task transcription system with a self-attention mechanism. The designed relative positional self-attention aims to model frame-wise short-term dependencies in audio and transcribe music of variable length. Adding the learnable attention mask on multiple attention head, the network can obtain different multi-scale attention distances for each subtask. Experiments on the MAESTRO dataset show the proposed system with the local relative time attention mechanism achieves state-of-the-art transcription performance on both frame and note metrics (frame F1 93.40%, note with offset F1 88.50%). |
| Author | Wang, Qi Liu, Mingkuan Chen, Xianhong Xiong, Mengwen |
| Author_xml | – sequence: 1 givenname: Qi surname: Wang fullname: Wang, Qi email: wangqi91@bjut.edu.cn organization: Beijing University of Technology,China – sequence: 2 givenname: Mingkuan surname: Liu fullname: Liu, Mingkuan email: chenxianhong@bjut.edu.cn organization: Beijing University of Technology,China – sequence: 3 givenname: Xianhong surname: Chen fullname: Chen, Xianhong email: wenmeng.xiong@bjut.edu.cn organization: Beijing University of Technology,China – sequence: 4 givenname: Mengwen surname: Xiong fullname: Xiong, Mengwen email: liumkuan@emails.bjut.edu.cn organization: Beijing University of Technology,China |
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| PublicationTitle | Proceedings ... Asia-Pacific Signal and Information Processing Association Annual Summit and Conference APSIPA ASC ... (Online) |
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| Snippet | Automatic music transcription (AMT) is to transcribe music audios into musical symbol representations. Recently, the Transformer-based transcription systems... |
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| SubjectTerms | Aggregates Asia Estimation Information processing Measurement Music Symbols |
| Title | Multi-task Piano Transcription with Local Relative Time Attention |
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