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arXiv · 2503.07977

Boundary Regression for Leitmotif Detection in Music Audio

Sihun Lee·Dasaem Jeong
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Abstract

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.

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Sihun Lee, Dasaem Jeong. 2025-03-11. Boundary Regression for Leitmotif Detection in Music Audio. https://arxiv.org/abs/2503.07977

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