In this paper, an exact subspace method for fundamental frequency is presented. The method is based on the principles of the MUSIC algorithm, wherein the orthogonality between the signal and and noise subspace is exploited. Unlike the original MUSIC algorithm, the new method does not employ an approximate measure of the angles between the subspaces but rather uses an exact measure. This makes a difference, for example, when the fundamental frequency is low, for real signals, or when the number

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... f samples is low. In Monte Carlo simulations, the performance of the new method is compared to a number of state-of-the-art methods and is demonstrated to lead to improvements in certain, critical cases.