A quantitative comparison of non-parametric time-frequency representations

L. Rankine; N. Stevenson; M. Mesbah; B. Boashash

A quantitative comparison of non-parametric time-frequency representations

L. Rankine^*, N. Stevenson, M. Mesbah, B. Boashash

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

In this paper we compare a variety of non-parametric time-frequency methods to determine the best time-frequency representation (TFR) for a collection of signals. These methods include quadratic time-frequency transforms, atomic decomposition and adaptive quadratic time-frequency transforms. The performance measures used to assess the TFRs include; two-dimensional correlation, IF correlation and time-frequency resolution. Synthetic signals with different time-frequency characteristics were used in the comparison to show the strengths and weaknesses of the different time-frequency methods. It was determined that adaptive quadratic time-frequency representations provide the best overall performance and should be used if no a priori information about the time-frequency characteristics of a signal is known.

Original language	English
Title of host publication	13th European Signal Processing Conference, EUSIPCO 2005
Pages	588-591
Number of pages	4
Publication status	Published - 2005
Externally published	Yes
Event	13th European Signal Processing Conference, EUSIPCO 2005 - Antalya, Turkey Duration: Sept 4 2005 → Sept 8 2005

Publication series

Name	13th European Signal Processing Conference, EUSIPCO 2005

Other

Other	13th European Signal Processing Conference, EUSIPCO 2005
Country/Territory	Turkey
City	Antalya
Period	9/4/05 → 9/8/05

ASJC Scopus subject areas

Signal Processing

Cite this

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title = "A quantitative comparison of non-parametric time-frequency representations",

abstract = "In this paper we compare a variety of non-parametric time-frequency methods to determine the best time-frequency representation (TFR) for a collection of signals. These methods include quadratic time-frequency transforms, atomic decomposition and adaptive quadratic time-frequency transforms. The performance measures used to assess the TFRs include; two-dimensional correlation, IF correlation and time-frequency resolution. Synthetic signals with different time-frequency characteristics were used in the comparison to show the strengths and weaknesses of the different time-frequency methods. It was determined that adaptive quadratic time-frequency representations provide the best overall performance and should be used if no a priori information about the time-frequency characteristics of a signal is known.",

author = "L. Rankine and N. Stevenson and M. Mesbah and B. Boashash",

year = "2005",

language = "English",

isbn = "1604238216",

series = "13th European Signal Processing Conference, EUSIPCO 2005",

pages = "588--591",

booktitle = "13th European Signal Processing Conference, EUSIPCO 2005",

note = "13th European Signal Processing Conference, EUSIPCO 2005 ; Conference date: 04-09-2005 Through 08-09-2005",

}

TY - GEN

T1 - A quantitative comparison of non-parametric time-frequency representations

AU - Rankine, L.

AU - Stevenson, N.

AU - Mesbah, M.

AU - Boashash, B.

PY - 2005

Y1 - 2005

N2 - In this paper we compare a variety of non-parametric time-frequency methods to determine the best time-frequency representation (TFR) for a collection of signals. These methods include quadratic time-frequency transforms, atomic decomposition and adaptive quadratic time-frequency transforms. The performance measures used to assess the TFRs include; two-dimensional correlation, IF correlation and time-frequency resolution. Synthetic signals with different time-frequency characteristics were used in the comparison to show the strengths and weaknesses of the different time-frequency methods. It was determined that adaptive quadratic time-frequency representations provide the best overall performance and should be used if no a priori information about the time-frequency characteristics of a signal is known.

AB - In this paper we compare a variety of non-parametric time-frequency methods to determine the best time-frequency representation (TFR) for a collection of signals. These methods include quadratic time-frequency transforms, atomic decomposition and adaptive quadratic time-frequency transforms. The performance measures used to assess the TFRs include; two-dimensional correlation, IF correlation and time-frequency resolution. Synthetic signals with different time-frequency characteristics were used in the comparison to show the strengths and weaknesses of the different time-frequency methods. It was determined that adaptive quadratic time-frequency representations provide the best overall performance and should be used if no a priori information about the time-frequency characteristics of a signal is known.

UR - http://www.scopus.com/inward/record.url?scp=84863656461&partnerID=8YFLogxK

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M3 - Conference contribution

AN - SCOPUS:84863656461

SN - 1604238216

SN - 9781604238211

T3 - 13th European Signal Processing Conference, EUSIPCO 2005

SP - 588

EP - 591

BT - 13th European Signal Processing Conference, EUSIPCO 2005

T2 - 13th European Signal Processing Conference, EUSIPCO 2005

Y2 - 4 September 2005 through 8 September 2005

ER -

A quantitative comparison of non-parametric time-frequency representations

Abstract

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Other

ASJC Scopus subject areas

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