Designing high-resolution time–frequency and time–scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance

2018 ◽  
Vol 77 ◽  
pp. 120-152 ◽  
Author(s):  
Boualem Boashash ◽  
Samir Ouelha
Keyword(s):  
High Resolution ◽  
Time Scale ◽  
Resolution Time ◽  
Time Frequency ◽  
Stationary Signals

Machine Learning Classification of Cohen's Class Time-Frequency Representations of Non-Stationary Signals: Effects on Earthquake Detection

2021 ◽  
Author(s):  
Marko Njirjak ◽  
Erik Otović ◽  
Dario Jozinović ◽  
Jonatan Lerga ◽  
Goran Mauša ◽  
...  
Keyword(s):  
Machine Learning ◽  
Original Data ◽  
Stationary Time Series ◽  
Waveform Data ◽  
Time Frequency ◽  
Machine Learning Classification ◽  
Seismic Waveforms ◽  
Stationary Signals ◽  
Non Stationary Signal

<p>The analysis of non-stationary signals is often performed on raw waveform data or on Fourier transformations of those data, i.e., spectrograms. However, the possibility of alternative time-frequency representations being more informative than spectrograms or the original data remains unstudied. In this study, we tested if alternative time-frequency representations could be more informative for machine learning classification of seismic signals. This hypothesis was assessed by training three well-established convolutional neural networks, using nine different time-frequency representations, to classify seismic waveforms as earthquake or noise. The results were compared to the base model, which was trained on the raw waveform data. The signals used in the experiment were seismogram instances from the LEN-DB seismological dataset (Magrini et al. 2020). The results demonstrate that Pseudo Wigner-Ville and Wigner-Ville time-frequency representations yield significantly better results than the base model, while Margenau-Hill performs significantly worse (P < .01). Interestingly, the spectrogram, which is often used in non-stationary signal analysis, did not yield statistically significant improvements. This research could have a notable impact in the field of seismology because the data that were previously hidden in the seismic noise are now classified more accurately. Moreover, the results might suggest that alternative time-frequency representations could be used in other fields which use non-stationary time series to extract more valuable information from the original data. The potential fields encompass different fields of geophysics, speech recognition, EEG and ECG signals, gravitational waves and so on. This, however, requires further research.</p>

Rayleigh wave separation using high‐resolution time‐frequency polarization filter

2020 ◽  
Vol 68 (7) ◽  
pp. 2104-2118
Author(s):  
Mohsen Kazemnia Kakhki ◽  
Webe J. Mansur ◽  
Franciane C. Peters
Keyword(s):  
High Resolution ◽  
Rayleigh Wave ◽  
Resolution Time ◽  
Polarization Filter ◽  
Time Frequency ◽  
Wave Separation

EEG Analysis

2017 ◽  
Author(s):  
Fabrice Wendling ◽  
Marco Congendo ◽  
Fernando H. Lopes da Silva
Keyword(s):  
Matching Pursuit ◽  
Effective Connectivity ◽  
Eeg Signals ◽  
Model Decomposition ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Artifact Rejection ◽  
Transient Events

This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.

High resolution time-frequency representation for chirp signals using an adaptive system based on duffing oscillators

2016 ◽  
Vol 55 ◽  
pp. 32-43 ◽  
Author(s):  
Antonio H. Costa ◽  
Rogerio Enríquez-Caldera ◽  
Maribel Tello-Bello ◽  
Carlos R. Bermúdez-Gómez
Keyword(s):  
High Resolution ◽  
Adaptive System ◽  
Resolution Time ◽  
Time Frequency ◽  
Frequency Representation ◽  
Chirp Signals
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