基于投票的音高估计与时间与频率对齐及相关性感知选择
Voting-based Pitch Estimation with Temporal and Frequential Alignment and Correlation Aware Selection
AI总结：
本文提出了一种基于投票的音高估计方法，通过时间与频率对齐和相关性感知选择提升基频估计的鲁棒性和准确性。
摘要：
投票方法是一种用于基频估计的集成方法，尽管在实践中以其鲁棒性而闻名，但缺乏深入的理论研究。本文对这一技术进行了系统分析和改进。首先，我们为该方法的有效性提供了理论基础，解释了基频估计中的误差方差减少，并引用康多塞陪审团定理来解释有声/无声检测的准确性。为解决其实际限制，我们提出了两个关键改进：1）预投票对齐程序，用于纠正估计器之间的时间和频率偏差；2）一种贪心算法，用于根据误差相关性选择一个紧凑而有效的估计器子集。在语音、唱歌和音乐的多样化数据集上的实验表明，我们的方法在清洁条件下优于单个最先进的估计器，并在嘈杂环境中保持了有声/无声检测的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
