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
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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3193694
- author
- Jensen, Jesper
; Glentis, George-Othan
; Christensen, Mads
; Jakobsson, Andreas
LU
and Jensen, Sören
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Fundamental frequency estimation, data adaptive estimators, efficient algorithms, optimal filtering
- host publication
- 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
- conference location
- Bucharest, Romania
- conference dates
- 2012-08-27 - 2012-08-31
- 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
- 2016-04-01 10:52:53
- date last changed
- 2024-01-07 03:26:22
@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}},
keywords = {{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}},
url = {{http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6334068}},
year = {{2012}},
}