Fast and Statistically Efficient Fundamental Frequency Estimation

Jesper Kjær Nielsen; Tobias Lindstrøm Jensen; Jesper Rindom Jensen; Mads Græsbøll Christensen; Søren Holdt Jensen

doi:10.1109/ICASSP.2016.7471642

Fast and Statistically Efficient Fundamental Frequency Estimation

Jesper Kjær Nielsen, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, Søren Holdt Jensen

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

9 Citations (Scopus)

530 Downloads (Pure)

Abstract

Fundamental frequency estimation is a very important task in many
applications involving periodic signals. For computational reasons,
fast autocorrelation-based estimation methods are often used despite
parametric estimation methods having superior estimation accuracy.
However, these parametric methods are much more costly to run. In
this paper, we propose an algorithm which significantly reduces the
computational cost of an accurate maximum likelihood-based estimator
for real-valued data. The computational cost is reduced by exploiting
the matrix structure of the problem and by using a recursive
solver. Via benchmarks, we demonstrate that the computation time
is reduced by approximately two orders of magnitude. The proposed
fast algorithm is available for download online.

Original language	English
Title of host publication	Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Publication date	Mar 2016
Pages	86-90
ISBN (Electronic)	978-1-4799-9988-0
DOIs	https://doi.org/10.1109/ICASSP.2016.7471642
Publication status	Published - Mar 2016
Event	The 41st IEEE International Conference on Acoustics, Speech and Signal Processing - Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016 http://www.icassp2016.org/

Conference

Conference	The 41st IEEE International Conference on Acoustics, Speech and Signal Processing
Country/Territory	China
City	Shanghai
Period	20/03/2016 → 25/03/2016
Internet address	http://www.icassp2016.org/

Series	I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN	1520-6149

Access to Document

10.1109/ICASSP.2016.7471642

ICASSP_paperAccepted author manuscript, 232 KB

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

Nielsen, Jesper Kjær ; Jensen, Tobias Lindstrøm ; Jensen, Jesper Rindom et al. / Fast and Statistically Efficient Fundamental Frequency Estimation. Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE (Institute of Electrical and Electronics Engineers), 2016. pp. 86-90 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

@inproceedings{ac00834b60db4f849ba71606fea36a6c,

title = "Fast and Statistically Efficient Fundamental Frequency Estimation",

abstract = "Fundamental frequency estimation is a very important task in manyapplications involving periodic signals. For computational reasons,fast autocorrelation-based estimation methods are often used despiteparametric estimation methods having superior estimation accuracy.However, these parametric methods are much more costly to run. Inthis paper, we propose an algorithm which significantly reduces thecomputational cost of an accurate maximum likelihood-based estimatorfor real-valued data. The computational cost is reduced by exploitingthe matrix structure of the problem and by using a recursivesolver. Via benchmarks, we demonstrate that the computation timeis reduced by approximately two orders of magnitude. The proposedfast algorithm is available for download online.",

keywords = "fundamental frequency estimation, Toeplitz-plus-Hankel solver, fast algorithm",

author = "Nielsen, {Jesper Kj{\ae}r} and Jensen, {Tobias Lindstr{\o}m} and Jensen, {Jesper Rindom} and Christensen, {Mads Gr{\ae}sb{\o}ll} and Jensen, {S{\o}ren Holdt}",

year = "2016",

month = mar,

doi = "10.1109/ICASSP.2016.7471642",

language = "English",

series = "I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings",

publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

pages = "86--90",

booktitle = "Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on",

address = "United States",

note = "The 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",

url = "http://www.icassp2016.org/",

}

Nielsen, JK, Jensen, TL, Jensen, JR , Christensen, MG & Jensen, SH 2016, Fast and Statistically Efficient Fundamental Frequency Estimation. in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE (Institute of Electrical and Electronics Engineers), I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, pp. 86-90, The 41st IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 20/03/2016. https://doi.org/10.1109/ICASSP.2016.7471642

Fast and Statistically Efficient Fundamental Frequency Estimation. / Nielsen, Jesper Kjær; Jensen, Tobias Lindstrøm; Jensen, Jesper Rindom et al.
Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE (Institute of Electrical and Electronics Engineers), 2016. p. 86-90 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Fast and Statistically Efficient Fundamental Frequency Estimation

AU - Nielsen, Jesper Kjær

AU - Jensen, Tobias Lindstrøm

AU - Jensen, Jesper Rindom

AU - Christensen, Mads Græsbøll

AU - Jensen, Søren Holdt

PY - 2016/3

Y1 - 2016/3

N2 - Fundamental frequency estimation is a very important task in manyapplications involving periodic signals. For computational reasons,fast autocorrelation-based estimation methods are often used despiteparametric estimation methods having superior estimation accuracy.However, these parametric methods are much more costly to run. Inthis paper, we propose an algorithm which significantly reduces thecomputational cost of an accurate maximum likelihood-based estimatorfor real-valued data. The computational cost is reduced by exploitingthe matrix structure of the problem and by using a recursivesolver. Via benchmarks, we demonstrate that the computation timeis reduced by approximately two orders of magnitude. The proposedfast algorithm is available for download online.

AB - Fundamental frequency estimation is a very important task in manyapplications involving periodic signals. For computational reasons,fast autocorrelation-based estimation methods are often used despiteparametric estimation methods having superior estimation accuracy.However, these parametric methods are much more costly to run. Inthis paper, we propose an algorithm which significantly reduces thecomputational cost of an accurate maximum likelihood-based estimatorfor real-valued data. The computational cost is reduced by exploitingthe matrix structure of the problem and by using a recursivesolver. Via benchmarks, we demonstrate that the computation timeis reduced by approximately two orders of magnitude. The proposedfast algorithm is available for download online.

KW - fundamental frequency estimation

KW - Toeplitz-plus-Hankel solver

KW - fast algorithm

U2 - 10.1109/ICASSP.2016.7471642

DO - 10.1109/ICASSP.2016.7471642

M3 - Article in proceeding

T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings

SP - 86

EP - 90

BT - Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on

PB - IEEE (Institute of Electrical and Electronics Engineers)

T2 - The 41st IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 20 March 2016 through 25 March 2016

ER -

Nielsen JK, Jensen TL, Jensen JR , Christensen MG, Jensen SH. Fast and Statistically Efficient Fundamental Frequency Estimation. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE (Institute of Electrical and Electronics Engineers). 2016. p. 86-90. (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings). doi: 10.1109/ICASSP.2016.7471642

Fast and Statistically Efficient Fundamental Frequency Estimation

Abstract

Conference

Access to Document

AUB Link

Other files and links

Fingerprint

RTC: Computational Oriented Real-time Convex Optimization in Signal Processing

Localization and Tracking of Speech - a Joint Audio-Visual Approach

Spatio-Temporal Filtering Methods for Enhancement and Separation of Speech Signals

Cite this

Fast and Statistically Efficient Fundamental Frequency Estimation

Abstract

Conference

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

RTC: Computational Oriented Real-time Convex Optimization in Signal Processing

Localization and Tracking of Speech - a Joint Audio-Visual Approach

Spatio-Temporal Filtering Methods for Enhancement and Separation of Speech Signals

Cite this