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ICASSP
2011
IEEE

A single snapshot optimal filtering method for fundamental frequency estimation

8 years 7 months ago
A single snapshot optimal filtering method for fundamental frequency estimation
Recently, optimal linearly constrained minimum variance (LCMV) filtering methods have been applied for fundamental frequency estimation. Like many other fundamental frequency estimators, these methods utilize the inverse covariance matrix. Therefore, the covariance matrix needs to be invertible which is typically ensured by using the sample covariance matrix involving data partitioning. The partitioning adversely affects the spectral resolution. We propose a novel optimal filtering method which utilizes the LCMV principle in conjunction with the iterative adaptive approach (IAA). The IAA enables us to estimate the covariance matrix from a single snapshot, i.e., without data partitioning. The experimental results show, that the performance of the proposed method is comparable or better than that of other competing methods in terms of spectral resolution.
Jesper Rindom Jensen, Mads Groesboll Christensen,
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Jesper Rindom Jensen, Mads Groesboll Christensen, Søren Holdt Jensen
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