An automatic fast optimization of Quadratic Time-frequency Distribution using the hybrid genetic algorithm

Awal, Md. Abdul and Boashash, Boualem (2017) An automatic fast optimization of Quadratic Time-frequency Distribution using the hybrid genetic algorithm. Signal Processing, 131 134-142. doi:10.1016/j.sigpro.2016.08.017


Author Awal, Md. Abdul
Boashash, Boualem
Title An automatic fast optimization of Quadratic Time-frequency Distribution using the hybrid genetic algorithm
Journal name Signal Processing   Check publisher's open access policy
ISSN 0165-1684
1872-7557
Publication date 2017-02-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.sigpro.2016.08.017
Open Access Status Not yet assessed
Volume 131
Start page 134
End page 142
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Abstract This paper presents a novel framework for a fully automatic optimization of Quadratic Time-frequency Distributions (QTFDs). This ‘black box’ approach automatically adjusts the QTFD kernel parameters by using a hybrid genetic algorithm (HGA). This results in an optimal use of QTFDs suitable for non-specialist users without requiring any additional input except for the signal itself. This optimization problem has been formulated as the minimization of the cost function of a modified energy concentration measure. The efficiency of the proposed method has been demonstrated by representing selected non-stationary signals in the time-frequency domain and testing robustness under different SNR conditions by estimating the instantaneous frequency. A fast implementation of QTFD optimization reduces computation time significantly; e.g., the computation time of a real world bat signal of 400 samples reduces to 3.5885±0.3942 s from its standard implementation (53.0910±1.445 s).
Keyword Energy concentration measure
Gradient descent
HGA
QTFD
Time-frequency optimization
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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