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In this paper, we present a subspace-based fundamental fre-quency estimator based on an extension of the MUSIC spec-tral estimator. A noise subspace is obtained from the eigen-value decomposition of the estimated sample covariance ma-trix and fundamental frequency candidates are selected as the frequencies where the harmonic signal subspace is closest to being orthogonal to the noise subspace. The performance of the proposed method is evaluated and compared to that of the non-linear least-squares (NLS) estimator and the corre-sponding Cramér-Rao bound; it is concluded that the pro-posed method has good statistical performance at a lower computational cost than the statistically efficient NLS esti-mator. 1