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arXiv · 2602.01727

Voting-based Pitch Estimation with Temporal and Frequential Alignment and Correlation Aware Selection

Junya Koguchi·Tomoki Koriyama
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Abstract

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.

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Junya Koguchi, Tomoki Koriyama. 2026-02-02. Voting-based Pitch Estimation with Temporal and Frequential Alignment and Correlation Aware Selection. https://arxiv.org/abs/2602.01727

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