A robust and computationally efficient subspace-based fundamental frequency estimator

Authors:
Johan Xi Zhang;Mads Græsbøll Christensen;Søren Holdt Jensen;Marc Moonen
Affiliations:
Department of Electronic Systems, Aalborg University, Aalborg, Denmark;Department of Media Technology, Aalborg University, Aalborg, Denmark;Department of Electronic Systems, Aalborg University, Aalborg, Denmark;Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
IEEE Transactions on Audio, Speech, and Language Processing
Year:
2010

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Abstract

This paper presents a method for high-resolution fundamental frequency (Fo) 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 Fo estimators, besides being robust against recorded data with inharmonicities.