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Computationally Efficient IAA-Based Estimation of the Fundamental Frequency

Jensen, Jesper; Glentis, George-Othan; Christensen, Mads; Jakobsson, Andreas LU and Jensen, Sören (2012) 20th European Signal Processing Conference, 2012 In Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European p.2163-2167
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Fundamental frequency estimation, data adaptive estimators, efficient algorithms, optimal filtering
in
Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
20th European Signal Processing Conference, 2012
external identifiers
  • scopus:84869845830
ISSN
2076-1465
2219-5491
ISBN
978-1-4673-1068-0 (print)
language
English
LU publication?
yes
id
dc23ee0b-8a40-4628-8e05-801e06381061 (old id 3193694)
alternative location
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6334068
date added to LUP
2013-05-07 17:20:00
date last changed
2017-04-16 03:18:23
@inproceedings{dc23ee0b-8a40-4628-8e05-801e06381061,
  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.},
  author       = {Jensen, Jesper and Glentis, George-Othan and Christensen, Mads and Jakobsson, Andreas and Jensen, Sören},
  booktitle    = {Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European},
  isbn         = {978-1-4673-1068-0 (print)},
  issn         = {2076-1465},
  keyword      = {Fundamental frequency estimation,data adaptive estimators,efficient algorithms,optimal filtering},
  language     = {eng},
  pages        = {2163--2167},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Computationally Efficient IAA-Based Estimation of the Fundamental Frequency},
  year         = {2012},
}