Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Robust audio watermarking using a chirp based technique
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
A new method for estimating the instantaneous frequency based on maximum likelihood
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
The Wigner distribution of noisy signals with adaptivetime-frequency varying window
IEEE Transactions on Signal Processing
Robust Wigner distribution with application to the instantaneousfrequency estimation
IEEE Transactions on Signal Processing
Modified Cohen-Lee time-frequency distributions and instantaneousbandwidth of multicomponent signals
IEEE Transactions on Signal Processing
Improved instantaneous frequency estimation using an adaptiveshort-time Fourier transform
IEEE Transactions on Signal Processing
Time--frequency feature representation using energy concentration: An overview of recent advances
Digital Signal Processing
Techniques to obtain good resolution and concentrated time-frequency distributions: a review
EURASIP Journal on Advances in Signal Processing
Statistical modeling and denoising Wigner-Ville distribution
Digital Signal Processing
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Analysis of nonstationary signals is a challenging task. True nonstationary signal analysis involves monitoring the frequency changes of the signal over time (i.e., monitoring the instantaneous frequency (IF) changes). The IF of a signal is traditionally obtained by taking the first derivative of the phase of the signal with respect to time. This poses some difficulties because the derivative of the phase of the signal may take negative values thus misleading the interpretation of instantaneous frequency. In this paper, a novel approach to extract the IF from its adaptive time-frequency distribution is proposed. The adaptive time-frequency distribution of a signal is obtained by decomposing the signal into components with good time-frequency localization and by combining the Wigner distribution of the components. The adaptive time-frequency distribution thus obtained is free of cross-terms and is a positive time-frequency distribution but it does not satisfy the marginal properties. The marginal properties are achieved by applying the minimum cross-entropy optimization to the time-frequency distribution. Then, IF may be obtained as the first central moment of this adaptive time-frequency distribution. The proposed method of IF estimation is very powerful for applications with low SNR. A set of real-world and synthetic signals of known IF dynamics is tested with the proposed method as well as with other common time-frequency distributions. The simulation shows that the method successfully extracted the IF of the signals.