Solution of the Problem of Classification of Hydroacoustic Signals Based on Harmonious Wavelets and Machine Learning

Two types of real hydroacoustic signals of whales are classified based on the harmonic wavelet transform (HWT) coefficients (fast implementation), windowed Fourier transform (FT) (spectrogram), and conventional FT using the k-NN algorithm. The accuracy of the classification is estimated for various...

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Veröffentlicht in:	Pattern recognition and image analysis Jg. 30; H. 3; S. 480 - 488
Hauptverfasser:	Klionskii, D. M., Kaplun, D. I., Voznesensky, A. S., Romanov, S. A., Levina, A. B., Bogaevskiy, D. V., Geppener, V. V., Razmochaeva, N. V.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.07.2020 Springer Nature B.V
Schlagworte:	Algorithms Applied Problems Classification Computer Science Fourier transforms Image Processing and Computer Vision Machine learning Neural networks Pattern Recognition Signal classification Signal processing Speech processing Underwater acoustics Wavelet transforms Whales windowed Fourier transform spectrogram neural networks wavelet transform k-NN algorithm Fourier transform harmonic wavelets classification machine learning
ISSN:	1054-6618, 1555-6212
Online-Zugang:	Volltext
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