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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.09788 |
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| _version_ | 1866912577469022208 |
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| author | Ru, Ganghui Wang, Jieying Zhao, Jiahao Wu, Yulun Yu, Yi Jiang, Nannan Wang, Wei Li, Wei |
| author_facet | Ru, Ganghui Wang, Jieying Zhao, Jiahao Wu, Yulun Yu, Yi Jiang, Nannan Wang, Wei Li, Wei |
| contents | Fine-tuning pre-trained foundation models has made significant progress in music information retrieval. However, applying these models to beat tracking tasks remains unexplored as the limited annotated data renders conventional fine-tuning methods ineffective. To address this challenge, we propose HingeNet, a novel and general parameter-efficient fine-tuning method specifically designed for beat tracking tasks. HingeNet is a lightweight and separable network, visually resembling a hinge, designed to tightly interface with pre-trained foundation models by using their intermediate feature representations as input. This unique architecture grants HingeNet broad generalizability, enabling effective integration with various pre-trained foundation models. Furthermore, considering the significance of harmonics in beat tracking, we introduce harmonic-aware mechanism during the fine-tuning process to better capture and emphasize the harmonic structures in musical signals. Experiments on benchmark datasets demonstrate that HingeNet achieves state-of-the-art performance in beat and downbeat tracking |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09788 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HingeNet: A Harmonic-Aware Fine-Tuning Approach for Beat Tracking Ru, Ganghui Wang, Jieying Zhao, Jiahao Wu, Yulun Yu, Yi Jiang, Nannan Wang, Wei Li, Wei Sound Audio and Speech Processing Fine-tuning pre-trained foundation models has made significant progress in music information retrieval. However, applying these models to beat tracking tasks remains unexplored as the limited annotated data renders conventional fine-tuning methods ineffective. To address this challenge, we propose HingeNet, a novel and general parameter-efficient fine-tuning method specifically designed for beat tracking tasks. HingeNet is a lightweight and separable network, visually resembling a hinge, designed to tightly interface with pre-trained foundation models by using their intermediate feature representations as input. This unique architecture grants HingeNet broad generalizability, enabling effective integration with various pre-trained foundation models. Furthermore, considering the significance of harmonics in beat tracking, we introduce harmonic-aware mechanism during the fine-tuning process to better capture and emphasize the harmonic structures in musical signals. Experiments on benchmark datasets demonstrate that HingeNet achieves state-of-the-art performance in beat and downbeat tracking |
| title | HingeNet: A Harmonic-Aware Fine-Tuning Approach for Beat Tracking |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.09788 |