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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07977 |
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| _version_ | 1866912269628080128 |
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| author | Lee, Sihun Jeong, Dasaem |
| author_facet | Lee, Sihun Jeong, Dasaem |
| contents | Leitmotifs are musical phrases that are reprised in various forms throughout a piece. Due to diverse variations and instrumentation, detecting the occurrence of leitmotifs from audio recordings is a highly challenging task. Leitmotif detection may be handled as a subcategory of audio event detection, where leitmotif activity is predicted at the frame level. However, as leitmotifs embody distinct, coherent musical structures, a more holistic approach akin to bounding box regression in visual object detection can be helpful. This method captures the entirety of a motif rather than fragmenting it into individual frames, thereby preserving its musical integrity and producing more useful predictions. We present our experimental results on tackling leitmotif detection as a boundary regression task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boundary Regression for Leitmotif Detection in Music Audio Lee, Sihun Jeong, Dasaem Sound Machine Learning Audio and Speech Processing I.2.0, I.2.1 Leitmotifs are musical phrases that are reprised in various forms throughout a piece. Due to diverse variations and instrumentation, detecting the occurrence of leitmotifs from audio recordings is a highly challenging task. Leitmotif detection may be handled as a subcategory of audio event detection, where leitmotif activity is predicted at the frame level. However, as leitmotifs embody distinct, coherent musical structures, a more holistic approach akin to bounding box regression in visual object detection can be helpful. This method captures the entirety of a motif rather than fragmenting it into individual frames, thereby preserving its musical integrity and producing more useful predictions. We present our experimental results on tackling leitmotif detection as a boundary regression task. |
| title | Boundary Regression for Leitmotif Detection in Music Audio |
| topic | Sound Machine Learning Audio and Speech Processing I.2.0, I.2.1 |
| url | https://arxiv.org/abs/2503.07977 |