The voting method, an ensemble approach for fundamental frequency estimation, is empirically known for its robustness but lacks thorough investigation. This paper provides a principled analysis and improvement of this technique. First, we offer a theoretical basis for its effectiveness, explaining the error variance reduction for fundamental frequency estimation and invoking Condorcet's jury theorem for voiced/unvoiced detection accuracy. To address its practical limitations, we propose two key improvements: 1) a pre-voting alignment procedure to correct temporal and frequential biases among estimators, and 2) a greedy algorithm to select a compact yet effective subset of estimators based on error correlation. Experiments on a diverse dataset of speech, singing, and music show that our proposed method with alignment outperforms individual state-of-the-art estimators in clean conditions and maintains robust voiced/unvoiced detection in noisy environments.

翻译：投票法作为一种基频估计的集成方法，虽经验证具有鲁棒性，但缺乏深入研究。本文对该技术进行了原理性分析与改进。首先，我们为其有效性提供了理论基础：解释了基频估计误差方差的降低机制，并引用孔多塞陪审团定理阐释了清浊音检测准确率的提升原理。针对其实际局限，我们提出了两项关键改进：1）一种投票前对齐流程，用于校正各估计器间的时序与频域偏差；2）一种基于误差相关性的贪心算法，用于选取紧凑而有效的估计器子集。在包含语音、歌唱与音乐的多样化数据集上的实验表明，采用对齐机制的所提方法在纯净环境下优于各先进独立估计器，并在噪声环境中保持了鲁棒的清浊音检测能力。