PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Applications and Comparison of Continuous Wavelet Transforms on Analysis of A-wave Impulse Noise

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Noise induced hearing loss (NIHL) is a serious occupational related health problem worldwide. The A-wave impulse noise could cause severe hearing loss, and characteristics of such kind of impulse noise in the joint time-frequency (T-F) domain are critical for evaluation of auditory hazard level. This study focuses on the analysis of A-wave impulse noise in the T-F domain using continual wavelet transforms. Three different wavelets, referring to Morlet, Mexican hat, and Meyer wavelets, were investigated and compared based on theoretical analysis and applications to experimental generated A-wave impulse noise signals. The underlying theory of continuous wavelet transform was given and the temporal and spectral resolutions were theoretically analyzed. The main results showed that the Mexican hat wavelet demonstrated significant advantages over the Morlet and Meyer wavelets for the characterization and analysis of the A-wave impulse noise. The results of this study provide useful information for applying wavelet transform on signal processing of the A-wave impulse noise.
Rocznik
Strony
503--512
Opis fizyczny
Bibliogr. 21 poz., rys., wykr.
Twórcy
autor
  • Department of Electrical and Computer Engineering, Southern Illinois University Carbondale, 1230 Lincoln Drive, Mail Code 6603, Carbondale, IL 62901, USA
autor
  • Department of Electrical and Computer Engineering, Southern Illinois University Carbondale, 1230 Lincoln Drive, Mail Code 6603, Carbondale, IL 62901, USA
Bibliografia
  • 1. Adeli H., Zhou Z., Dadmehr N. (2003), Analysis of EEG records in an epileptic patient using wavelet transform, Journal of Neuroscience Methods, 123, 1, 69–87.
  • 2. Agrawal Y., Platz E., Niparko J.K. (2008), Prevalence of hearing loss and differences by demographic characteristics among US adults: data from the National Health and Nutrition Examination Survey, 1999–2004, Arch. Intern. Med., 168, 14, 1522–1530.
  • 3. Chapa J.O., Rao R.M. (2000), Algorithms for designing wavelets to match a specified signal, IEEE Transactions on Signal Processing, 48, 12, 3395–3406.
  • 4. Clifford R.E., Rogers R.A. (2009), Impulse noise: theoretical solutions to the quandary of cochlear protection, Annals of Otology Rhinology and Laryngology, 118, 6, 417–427.
  • 5. Coifman R.R., Wickerhauser M.V. (1992), Entropy-based algorithms for best basis selection, IEEE Transactions on Information Theory, 38, 2, 713–718.
  • 6. Daubechies I. (1992), Ten Lectures on Wavelets, SIAM, Philadelphia.
  • 7. Hamernik R.P., Ahroon W.A., Hsueh K.D., Lei S.F., Davis R.I. (1993), Audiometric and histological differences between the effects of continuous and impulsive noise exposures, Journal of the Acoustical Society of America, 93, 4, 2088–2095.
  • 8. Henderson D., Hamernik R.P. (1986), Impulse noise – critical review, Journal of the Acoustical Society of America, 80, 2, 569–584.
  • 9. Kim J., Welcome D.E., Dong R.G., Song W.J., Hayden C. (2007), Time-frequency characterization of hand-transmitted, impulsive vibrations using analytic wavelet transform, Journal of Sound and Vibration, 308, 1, 98–111.
  • 10. Ilow J., Hatzinakos D. (1998), Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers, Signal Processing, IEEE Transactions on, 46, 6, 1601–1611.
  • 11. Lin J., Qu L.S. (2000), Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis, Journal of Sound and Vibration, 234, 1, 135–148.
  • 12. Mallat S. (1997), A wavelet Tour of Signal Processing, Academic Press, New York.
  • 13. Price G.R., Kim H.N., Lim D.J., Dunn D. (1989), Hazard from weapons impulses – histological and electrophysiological evidence, Journal of the Acoustical Society of America 85, 3, 1245–1254.
  • 14. Rosso O.A., Blanco S., Yordanova J., Kolev V., Fiqliola A., Schurmann M., Basar E. (2001), Wavelet entropy: a new tool for analysis of short duration brain electrical signals, Journal of Neuroscience Methods, 105, 1, 65–75.
  • 15. Satish L., Nazneen B. (2003), Wavelet-based denoising of partial discharge signals buried in excessive noise and interference, IEEE Transactions on Dielectrics and Electrical Insulation, 10, 2, 354–367.
  • 16. Senhadji L., Wendling F. (2002), Epileptic transient detection: wavelets and time-frequency approaches, Neurophysiologie Clinique-Clinical Neurophysiology, 32, 3, 175–192.
  • 17. Smith G. (1996), Noise? What noise?, Occupational Health & Safety, 65, 3, 38.
  • 18. Wang W.J., McFadden P.D. (1996), Application of wavelets to gearbox vibration signals for fault detection, Journal of Sound and Vibration, 192, 5, 927–939.
  • 19. Wu Q., Qin J. (2013), Effects of key parameters of impulse noise on prediction of the auditory hazard using AHAAH model, International Journal of Computational Biology and Drug Design, 6, 3, 210–220.
  • 20. Young R.K. (1993), Wavelet theory and its applications, Springer, New York.
  • 21. Zhu X.D., Kim J. (2006), Application of analytic wavelet transform to analysis of highly impulsive noises, Journal of Sound and Vibration, 294, 4, 841–855.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1d903c03-f0bf-4831-89e0-8467de74d8a8
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.