Open AccessWavelet-based audio features of DC motor sound
Author(s) -
Đorđe Damnjanović,
Dejan Ćirič,
Zoran Perić
Publication year - 2021
Publication title -
facta universitatis. series electronics and energetics/facta universitatis. series: electronics and energetics
Language(s) - English
Resource type - Journals
eISSN - 2217-5997
pISSN - 0353-3670
DOI - 10.2298/fuee2101071d
Subject(s) - wavelet , computer science , speech recognition , audio signal , wavelet transform , signal (programming language) , wavelet packet decomposition , pattern recognition (psychology) , representation (politics) , artificial intelligence , speech coding , politics , political science , law , programming language
The usage of wavelets is widespread in many fields nowadays, especially in signal processing. Their nature provides some advantages in comparison to the Fourier transform, and therefore many applications rely on wavelets rather than on other methods. The decomposition of wavelets into detail and approximation coefficients is one of the methods to extract representative audio features. They can be used in signal analysis and further classification. This paper investigates the usage of various wavelet families in the wavelet decomposition to extract audio features of direct current (DC) motor sounds recorded in the production environment. The purpose of feature representation and analysis is the detection of DC motor failures in motor production. The effects of applying different wavelet families and parameters in the decomposition process are studied using sounds of more than 60 motors. Time and frequency analysis is also done for the tested DC motor sounds.