Abstract
We explore transfer learning strategies for musical onset detection in the Afro-Brazilian Maracatu tradition, which features complex rhythmic patterns that challenge conventional models. We adapt two Temporal Convolutional Network architectures: one pre-trained for onset detection (intra-task) and another for beat tracking (inter-task). Using only 5-second annotated snippets per instrument, we fine-tune these models through layer-wise retraining strategies for five traditional percussion instruments. Our results demonstrate significant improvements over baseline performance, with F1 scores reaching up to 0.998 in the intra-task setting and improvements of over 50 percentage points in best-case scenarios. The cross-task adaptation proves particularly effective for time-keeping instruments, where onsets naturally align with beat positions. The optimal fine-tuning configuration varies by instrument, highlighting the importance of instrument-specific adaptation strategies. This approach addresses the challenges of underrepresented musical traditions, offering an efficient human-in-the-loop methodology that minimizes annotation effort while maximizing performance. Our findings contribute to more inclusive music information retrieval tools applicable beyond Western musical contexts.
Abstract (translated)
我们在非洲巴西马腊卡图传统中的音乐起音检测中探索了迁移学习策略，这种传统包含复杂的节奏模式，给常规模型带来了挑战。我们对两个时间卷积网络架构进行了调整：一个预先训练用于起音检测（任务内），另一个则用于节拍跟踪（跨任务）。仅使用每个乐器5秒的标注片段，通过逐层再训练策略对这两种模型进行微调，以适应五种传统打击乐器。我们的实验结果表明，在任务内的设置下，F1分数达到了0.998的显著改善，并且在最佳情况下比基线性能提高了超过50个百分点。跨任务调整对于节奏控制型乐器尤为有效，因为起音自然与节拍位置对齐。最有效的微调配置因乐器而异，这强调了针对特定乐器制定适应策略的重要性。这种做法解决了不被充分代表的音乐传统所面临的挑战，并提供了一种高效的人机协作方法，在最小化标注工作量的同时最大化性能。我们的发现有助于开发适用于非西方音乐背景下的更包容性音乐信息检索工具。
URL
https://arxiv.org/abs/2507.04858
