NDLI: Computationally efficient IAA-based estimation of the fundamental frequency

Content Provider	IEEE Xplore Digital Library
Author	Jensen, J.R. Glentis, G. Christensen, M.G. Jakobsson, A. Jensen, S.H.
Copyright Year	2012
Description	Author affiliation: University of Peloponnese, Dept. Telecommunications, Greece (Glentis, G.) \|\| Dept. of Electronic Systems, Aalborg University, Denmark (Jensen, J.R.; Jensen, S.H.) \|\| Mathematical Statistics, Lund University, Sweden (Jakobsson, A.) \|\| Dept. of Architecture, Design and Media Technology, Aalborg University, Denmark (Christensen, M.G.)
Abstract	Optimal linearly constrained minimum variance (LCMV) filtering methods have recently been applied to fundamental frequency estimation. Like many other fundamental frequency estimators, these methods are constructed using an estimate of the inverse data covariance matrix. The required matrix inverse is typically formed using the sample covariance matrix via data partitioning, although this is well-known to adversely affect the spectral resolution. In this paper, we propose a fast implementation of a novel optimal filtering method that utilizes the LCMV principle in conjunction with the iterative adaptive approach (IAA). The IAA formulation enables an accurate covariance matrix estimate from a single snapshot, i.e., without data partitioning, but the improvement comes at a notable computational cost. Exploiting the estimator's inherently low displacement rank of the necessary products of Toeplitz-like matrices, we form a computationally efficient implementation, reducing the required computational complexity with several orders of magnitude. 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.
Starting Page	2163
Ending Page	2167
File Size	134300
Page Count	5
File Format	PDF
ISBN	9781467310680
ISSN	20761465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-27
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	EURASIP
Subject Keyword	Estimation Fundamental frequency estimation Optimal filtering Harmonic analysis Speech Frequency estimation Vectors Covariance matrix Efficient algorithms Data adaptive estimators
Content Type	Text
Resource Type	Article

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