scholarly journals Application of wavelet transform for classification of underwater acoustic signals

2016 ◽  
Author(s):  
Noha Korany
Keyword(s):  
Wavelet Transform ◽  
Acoustic Signals ◽  
Underwater Acoustic

Denoising Underwater Acoustic Signals for Applications in Acoustical Oceanography

2017 ◽  
Vol 25 (02) ◽  
pp. 1750015 ◽  
Author(s):  
Michael Taroudakis ◽  
Costas Smaragdakis ◽  
N. Ross Chapman
Keyword(s):  
Wavelet Transform ◽  
Marine Environment ◽  
Acoustic Signals ◽  
Significant Part ◽  
Statistical Features ◽  
Underwater Acoustic ◽  
Signal Energy ◽  
Reliable Estimation ◽  
Signal Characterization ◽  
True Signal

A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.

scholarly journals The Free-Swimming Device Leakage Detection in Plastic Water-filled Pipes through Tuning the Wavelet Transform to the Underwater Acoustic Signals

Water ◽  
2017 ◽  
Vol 9 (10) ◽  
pp. 731 ◽  
Author(s):  
Dileep Kumar ◽  
Dezhan Tu ◽  
Naifu Zhu ◽  
Reehan Shah ◽  
Dibo Hou ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Acoustic Signals ◽  
Leakage Detection ◽  
Free Swimming ◽  
Underwater Acoustic

Fault Classification of IC Engine Using Wavelet Energy Features and Geometric Mean Neuron Model

2020 ◽  
Author(s):  
Mohd Shiblee
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Neuron Model ◽  
Geometric Mean ◽  
Acoustic Signals ◽  
Fault Classification ◽  
Ic Engine ◽  
Wavelet Energy ◽  
Novel Approach

The paper proposes a novel approach for fault classification in an Internal Combustion (IC) engine using wavelet energy features and geometric mean neuron model based neural networks. Live signals from the engine were collected with and without faults by using four industrial microphones. The acoustic signals measured for faulty engines were decomposed using wavelet transform. The energy of each decomposed signal was computed and used as a feature vector for further classification using GMN based neural networks.

On the classification of underwater acoustic signals. II. Experimental applications involving fish

1975 ◽  
Vol 58 (S1) ◽  
pp. S101-S101 ◽  
Author(s):  
L. M. Deuser ◽  
D. Middleton ◽  
T. D. Plemons ◽  
J. K. Vaughan
Keyword(s):  
Acoustic Signals ◽  
Underwater Acoustic

On the classification of underwater acoustic signals. II. Experimental applications involving fish

1979 ◽  
Vol 65 (2) ◽  
pp. 444-455 ◽  
Author(s):  
Larry M. Deuser ◽  
David Middleton ◽  
Terry D. Plemons ◽  
J. Kenneth Vaughan
Keyword(s):  
Acoustic Signals ◽  
Underwater Acoustic
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