This paper presents a method for high-resolution fundamental frequency ( F ₀) estimation based on subspaces decomposed from a frequency-selective data model, by effectively splitting the signal into a number of subbands. The resulting estimator is termed frequency-selective harmonic MUSIC (F-HMUSIC). The subband-based approach is expected to ensure computational savings and robustness. Additionally, a method for automatic subband signal activity detection is proposed, which is based on information-theoretic criterion where no subjective judgment is needed. The F-HMUSIC algorithm exhibits good statistical performance when evaluated with synthetic signals for both white and colored noises, while its evaluation on real-life audio signal shows the algorithm to be competitive with other estimators. Finally, F-HMUSIC is found to be computationally more efficient and robust than other subspace-based F ₀ estimators, besides being robust against recorded data with inharmonicities.