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.
View on arXiv@article{lee2025_2503.07977,
title={ Boundary Regression for Leitmotif Detection in Music Audio },
author={ Sihun Lee and Dasaem Jeong },
journal={arXiv preprint arXiv:2503.07977},
year={ 2025 }
}