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>

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

2021 ◽  
Vol 79 ◽  
pp. 52-58
Author(s):  
Arnaldo Stanzione ◽  
Renato Cuocolo ◽  
Francesco Verde ◽  
Roberta Galatola ◽  
Valeria Romeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Classification

scholarly journals Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06257
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Rubiel Vargas-Cañas ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Music Information Retrieval ◽  
Machine Learning Classification ◽  
Information Retrieval Methods ◽  
Music Information

scholarly journals Machine learning classification of new asteroid families members

2020 ◽  
Author(s):  
Valerio Carruba
Keyword(s):  
Machine Learning ◽  
Family Members ◽  
Classification Algorithms ◽  
New Members ◽  
Machine Learning Classification ◽  
New Family ◽  
Robotic Telescope ◽  
Traditional Approaches ◽  
Asteroid Families

<p>Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object.  These groups are mainly identified in proper elements or frequencies domains.   Because of robotic telescope surveys, the number of known asteroids has increased from about 10,000 in the early 90's to more than 750,000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may   struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a,e,sin(i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand alone and ensemble approaches.  The Extremely Randomized Trees (ExtraTree) method had the highest precision, enabling to  retrieve up to 97% of family members identified with standard HCM.</p>

scholarly journals Time-Frequency Feature Analysis

2021 ◽  
Author(s):  
Behnaz Ghoraani
Keyword(s):  
Pattern Recognition ◽  
Real World ◽  
Feature Detection ◽  
Classification Systems ◽  
Time Frequency ◽  
Analysis Methodology ◽  
Stationary Signals ◽  
Time Varying Frequency ◽  
Non Stationary Signal ◽  
Discriminative Pattern

Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very important in understanding the signal better, which could be of immense use in various applications such as pattern recognition and automated-decision making systems. In order to extract meaningful time-frequency (TF) features, a joint TF analysis is required. The proposed work is an attempt to develop a generalized TF analysis methodology that exploits the benefits of TF distribution (TFD) in pattern classification systems as related to discriminant feature detection and classification. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using adaptive and discriminant TF techniques. To fulfill this objective, in the first point, we build a novel TF matrix (TFM) decomposition that increases the effectiveness of segmentation in real-world signals. Instantaneous and unique features are extracted from each segment such that they successfully represent joint TF structure of the signal. In the second point, based on the above technique, two unique and novel discriminant TF analysis methods are proposed to perform an improved and discriminant feature selection of any non-stationary signals. The first approach is a new machine learning method that identifies the clusters of the discriminant features to compute the presence of the discriminative pattern in any given signal, and classify them accordingly. The second approach is a discriminant TFM (DTFM) framework, which is a combination of TFM decomposition and the discriminant clustering techniques. The developed DTFM analysis automatically identifies the differences between different classes as the distinguishing structure, and uses the identified structure to accurately classify and locate the discriminant structure in the signal. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The extracted TF features provide strong and successful characterization and classification of real and synthetic non-stationary signals. The proposed TF techniques facilitate the adaptation of TF quantification to any feature detection technique in automating the identification process of discriminatory TF features, and can find applications in many different fields including biomedical and multimedia signal processing.

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