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Investigations of Auditory Filters Based Excitation Patterns for Assessment of Noise Induced Hearing Loss

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Języki publikacji
EN
Abstrakty
EN
Noise induced hearing loss (NIHL) as one of the major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been considered as one of the mechanisms to estimate the movements of the basilar membrane (BM) in the cochlea. In this study, two auditory filters, dual resonance nonlinear (DRNL) filter and rounded-exponential (ROEX) filter are applied to create two EPs, the velocity EP and the loudness EP respectively. Two noise hazard metrics are proposed based on two EPs to evaluate hazardous levels caused by different types of noise. Moreover Gaussian noise and single-tone noise are simulated to evaluate performances of the proposed EPs and the noise metrics. The results show that both EPs can reflect the responses of the BM to different types of noise. For Gaussian noise there is a frequency shift between the velocity EP and the loudness EP. The results suggest that both EPs can be used for assessment of NIHL.
Rocznik
Strony
477--486
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wykr.
Twórcy
  • Department of Electrical and Computer Engineering, Mail Code 6603, 1230 Lincoln Drive, Carbondale, IL 62901, USA
autor
  • Department of Electrical and Computer Engineering, Mail Code 6603, 1230 Lincoln Drive, Carbondale, IL 62901, USA
autor
  • Department of Electrical and Computer Engineering, Mail Code 6603, 1230 Lincoln Drive, Carbondale, IL 62901, USA
Bibliografia
  • 1. ANSI (2005), American National Standard: procedure for the computation of loudness of steady sounds, Melville, N. Y.: Standards Secretariat, Acoustical Society of America.
  • 2. ANSI (2007), Procedure for the computation of loudness of steady sounds, ANSI S3, 4-2007.
  • 3. Calford M., Rajan R., Irvine D. (1993), Rapid changes in the frequency tuning of neurons in cat audi tory cortex resulting from pure-tone-induced temporary threshold shift, Neuroscience, 55, 4, 953-964.
  • 4. Chen Z., HuvG., Glasberg, B.vR., Moore B. C. (2011), A new method of calculating auditory excitation patterns and loudness for steady sounds, Hearing research, 282, 1, 204-215.
  • 5. Crane H. (1966), Mechanical impact: a model for audi tory excitation and fatigue. The Journal of the Acoustical Society of America, 40, 5, 1147-1159.
  • 6. Fletcher H. (1940), Auditory patterns, Reviews of Modern Physics, 12, 1, 47.
  • 7. Glasberg B. R., Moore B. C. (1990), Derivation of auditory filter shapes from notched-noise data, Hearing Research, 47, 1-2, 103-138.
  • 8. Hartmann W. M. (1997), Signals, sound, and sensation, Springer Science & Business Media.
  • 9. Hohmann V. (2002), Frequency analysis and synthesis using a Gammatone filterbank, Acta Acustica united with Acustica, 88, 3, 433-442.
  • 10. Hussain Z. M., Sadik A. Z., O’Shea P. (2011), Digital signal processing: an introduction with MATLAB and applications, Springer Science & Business Media.
  • 11. Irino T., Patterson R. D. (2006), A dynamic compressive gammachirp auditory filterbank, Audio, Speech, and Language Processing, IEEE Transactions on, 14, 6, 2222-2232.
  • 12. Kirchner D. B. et al. (2012), Occupational noiseinduced hearing loss: ACOEM Task Force on occupational hearing loss, Journal of Occupational and Environmental Medicine, 54, 1, 106-108.
  • 13. Lopez-Poveda E. A., Meddis R. (2001), A human nonlinear cochlear filterbank, The Journal of the Acoustical Society of America, 110, 6, 3107-3118.
  • 14. Meddis R., O’Mard L., Lopez-Poveda E. A. (2001), A computational algorithm for computing nonlinear auditory frequency selectivity The Journal of the Acoustical Society of America, 109, 6, 2852-2861.
  • 15. Moore B. C., Glasberg B. R. (1983), Suggested formule for calculating auditory-filter bandwidths and excitation patterns, The Journal of the Acoustical Society of America, 74, 3, 750-753.
  • 16. Moore B. C., Glasberg B. R., Baer T. (1997), A model for the prediction of thresholds, loudness, and partial loudness, Journal of the Audio Engineering Society, 45, 4, 224-240.
  • 17. Ohlemiller K. K. (2006), Contributions of mouse models to understanding of age-and noise-related heating loss, Brain Research, 1091, 1, 89-102.
  • 18. Patterson R. D. (1976), Auditory filter shapes derived with noise stimuli, The Journal of the Acoustical Society of America, 59, 3, 640-654.
  • 19. Qin J., Jiang Y., Mahdi A. (2014), Recent developments on noise induced hearing loss for military and industrial applications, Biosensors Journal, 3, e101.
  • 20. Qin J., Sun P., Walker J. (2014), Measurement of field complex noise using a novel acoustic detection system, Paper presented at the AUTOTESTCON 2014, IEEE.
  • 21. Rabinowitz P. M. (2000), Noise-induced hearing loss, American Family Physician, 61, 9, 2759-2760.
  • 22. Saremi A., Beutelmann R., Dietz M., Ashida G., Kretzberg J., Verhulst S. (2016), A comparative study of seven human cochlear filter models, The Journal of the Acoustical Society of America, 140, 3, 1618-1634.
  • 23. Schomer P. D., Suzuki Y., Saito F. (2001), Evaluation of loudness-level weightings for assessing the annoyance of environmental noise, The Journal of the Acoustical Society of America, 110, 5, 2390-2397.
  • 24. Sun P., Fox D., Campbell K., Qin J. (2017), Auditory fatigue model applications to predict noise induced hearing loss in human and chinchilla, Applied Acoustics, 119, 57-65.
  • 25. Sun P., Qin J. (2016a), Auditory fatigue models for prediction of gradually developed noise induced heating loss, Paper presented at the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).
  • 26. Sun P., Qin J. (2016b), Excitation patterns of two auditory models applied for noise induced hearing loss assessment, Paper presented at the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).
  • 27. Sun P., Qin J., Campbell K. (2015), Fatigue Modeling via Mammalian Auditory System for prediction of noise induced hearing loss, Computational and Mathematical Methods in Medicine, 2015, Article ID 753864, 13 pages, http://dx.doi.org/10.1155/2015/753864.
  • 28. Sun P., Qin J., Qiu W. (2016), Development and validation of a new adaptive weighting for auditory risk assessment of complex noise, Applied Acoustics, 103, 30-36.
  • 29. Tak S., Davis R. R., Calvert G. M. (2009), Exposure to hazardous workplace noise and use of hearing protection devices among US Wolkers-NHANES, 1999-2004, American Journal of Industrial Medicine, 52, 5, 358-371.
  • 30. Wu Q., Qin J. (2013), Effects of key parameters of impulse noise on prediction of the auditory hazard Rusing AHAAH model, International Journal of Computational Biology ond Drug Design, 6, 3, 210-220.
  • 31. Zwicker E. (1970), Masking and psychological excitation as consequences of the ear’s frequency analysis, [in:] Frequency Analysis and Periodicity Detection In Hearing, R. Plomp, G. F. Smoorenburg [Eds.], pp. 376-394, Sijthoff, Leiden.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-321bb31b-4cd6-409d-8a96-dd735b2d0624
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