用边界回归方法精准定位音乐中的主题旋律片段。
Boundary Regression for Leitmotif Detection in Music Audio
- 将主题旋律检测视为边界回归任务，而非逐帧预测。
- 通过完整捕捉旋律结构，提升检测准确性与音乐连贯性。
- 适合音乐分析、智能作曲与影视配乐研究者使用。
主题旋律是贯穿作品始终的音乐短语，因其多样的变体和配器变化，从音频中检测其出现极具挑战性。传统方法将其视为音频事件检测，在帧级别预测主题活动。但主题旋律具有完整且连贯的音乐结构，借鉴视觉目标检测中的边界框回归思路，可更全面地捕获旋律整体特征，保留其音乐完整性并生成更实用的预测结果。本文实验验证了将主题旋律检测建模为边界回归任务的有效性。
原文摘要 · Abstract (English)
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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