用元学习解决印度塔布拉鼓数据少难题，快速识别鼓点与节奏型
Meta-learning-based percussion transcription and $t\bar{a}la$ identification from low-resource audio
- 基于MAML的元学习框架，小样本下快速适应新任务
- 在少量标注数据下，鼓点转录准确率显著优于现有方法
- 适用于印度与西方打击乐，助力传统音乐数字化研究
本研究提出一种基于元学习的低资源塔布拉鼓击打转录（TST）与塔拉（tāla）识别方法。针对标注数据稀缺和标签不一致的问题，采用模型无关元学习（MAML），实现仅用少量数据即可快速适应新任务。该方法在多种数据集上验证，包括独奏与合奏录音，在多声部音频中表现出强鲁棒性。提出了两种基于击打序列与节奏模式的新型塔拉识别技术。此外，该方法在自动鼓点转录（ADT）任务中也表现优异，展现出对印度与西方打击乐的通用性。实验表明，在低资源条件下，该方法显著优于现有技术，为音乐转录及传统音乐计算研究提供有力工具。
原文摘要 · Abstract (English)
This study introduces a meta-learning-based approach for low-resource Tabla Stroke Transcription (TST) and $t\bar{a}la$ identification in Hindustani classical music. Using Model-Agnostic Meta-Learning (MAML), we address the challenges of limited annotated datasets and label heterogeneity, enabling rapid adaptation to new tasks with minimal data. The method is validated across various datasets, including tabla solo and concert recordings, demonstrating robustness in polyphonic audio scenarios. We propose two novel $t\bar{a}la$ identification techniques based on stroke sequences and rhythmic patterns. Additionally, the approach proves effective for Automatic Drum Transcription (ADT), showcasing its flexibility for Indian and Western percussion music. Experimental results show that the proposed method outperforms existing techniques in low-resource settings, significantly contributing to music transcription and studying musical traditions through computational tools.
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