分离不确定性提升旋律估计的主动学习效率
Improving Active Learning for Melody Estimation by Disentangling Uncertainties
- 分离随机与认知不确定性指导主动学习
- 认知不确定性在领域适配中更可靠，标签需求减少
- 适合音乐信息检索与少样本学习研究者
旋律估计是音乐信息检索（MIR）的核心任务。尽管已有研究探索信号处理、机器学习及深度学习方法，近期更关注主动学习中不确定性的利用。然而，现有方法未探究不同不确定性类型的相对有效性。本文采用解耦随机不确定性与认知不确定性框架，指导旋律估计的主动学习。模型在源数据集上训练后，仅需少量标注样本即可适应新领域。实验表明，相较于随机不确定性，认知不确定性在减少标注成本的前提下，对领域适配更具可靠性。
原文摘要 · Abstract (English)
Estimating the fundamental frequency, or melody, is a core task in Music Information Retrieval (MIR). Various studies have explored signal processing, machine learning, and deep-learning-based approaches, with a very recent focus on utilizing uncertainty in active learning settings for melody estimation. However, these approaches do not investigate the relative effectiveness of different uncertainties. In this work, we follow a framework that disentangles aleatoric and epistemic uncertainties to guide active learning for melody estimation. Trained on a source dataset, our model adapts to new domains using only a small number of labeled samples. Experimental results demonstrate that epistemic uncertainty is more reliable for domain adaptation with reduced labeling effort as compared to aleatoric uncertainty.
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