Robust Subspace-based Fundamental Frequency Estimation

The problem of fundamental frequency estimation is considered in the context of signals where the frequencies of the harmonics are not exact integer multiples of a fundamental frequency. This frequently occurs in audio signals produced by, for example, stiff-stringed musical instruments, and is sometimes referred to as inharmonicity. We derive a novel robust method based on the subspace orthogonality property of MUSIC and show how it may be used for analyzing audio signals. The proposed method is both more general and less complex than a straight-forward implementation of a parametric model of the inharmonicity derived from a physical instrument model. Additionally, it leads to more accurate estimates of the individual frequencies than the method based on the parametric inharmonicity model and a reduced bias of the fundamental frequency compared to the perfectly harmonic model.