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 |
| Event | 13th European Signal Processing Conference, EUSIPCO 2005 - Antalya, Turkey Duration: Sep 4 2005 → Sep 8 2005 |
Other
| Other | 13th European Signal Processing Conference, EUSIPCO 2005 |
|---|---|
| Country | Turkey |
| City | Antalya |
| Period | 9/4/05 → 9/8/05 |
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ASJC Scopus subject areas
- Signal Processing
Cite this
A quantitative comparison of non-parametric time-frequency representations. / Rankine, L.; Stevenson, N.; Mesbah, M.; Boashash, B.
13th European Signal Processing Conference, EUSIPCO 2005. 2005. p. 588-591.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
UR - http://www.scopus.com/inward/citedby.url?scp=84863656461&partnerID=8YFLogxK
M3 - Conference contribution
SN - 1604238216
SN - 9781604238211
SP - 588
EP - 591
BT - 13th European Signal Processing Conference, EUSIPCO 2005
ER -