置信度引导的马尔可夫加权用于半监督塔布拉鼓击奏转录
Confidence-Guided Markov Weighting for Semi-Supervised Tabla Stroke Transcription
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中文总结 AI 辅助
提出半监督框架，用置信度引导的马尔可夫加权改进塔布拉鼓击奏转录，通过教师模型和击奏级置信度估计提升伪标签质量，实验验证优于传统方法。
中文摘要 AI 辅助
塔布拉鼓击奏转录（TST）将塔布拉鼓音频转换为符号化的击奏序列，但标注录音的稀缺使得全监督训练具有挑战性。我们提出了一种半监督框架，利用序列级标注和未标注的塔布拉鼓录音。教师模型生成伪标签序列，而击奏级置信度估计模型（S-CEM）估计每个预测击奏的置信度。为改善不确定伪标签的训练，我们提出了置信度引导的马尔可夫加权替代时序分类（CMW-ATC），该算法利用学习到的击奏转移和可靠的邻近击奏，对不确定跨度内的候选序列进行加权。在三种TST评估设置下的实验表明，与传统的师生训练及其替代形式的替代时序分类（ATC-R）相比，该方法取得了一致的改进。消融研究进一步展示了S-CEM、马尔可夫转移加权以及使用不确定跨度两侧可靠击奏的贡献。
英文摘要
Tabla Stroke Transcription (TST) converts tabla audio into symbolic stroke sequences, but the scarcity of annotated recordings makes fully supervised training challenging. We propose a semi-supervised framework that uses sequence-level labelled and unlabelled tabla recordings. A teacher model generates pseudo-label sequences, while a Stroke-Level Confidence Estimation Model (S-CEM) estimates confidence for each predicted stroke. To improve training with uncertain pseudo-labels, we propose Confidence-Guided Markov Weighted Alternative Temporal Classification (CMW-ATC), which weights candidate sequences within uncertain spans using learned stroke transitions and reliable neighbouring strokes. Experiments across three TST evaluation settings show consistent improvements over conventional teacher--student training and Alternative Temporal Classification in its replacement form (ATC-R). Ablation studies further show the contributions of S-CEM, Markov transition weighting, and the use of reliable strokes on both sides of uncertain spans.
