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Improving the Performance of Noise Reduction in Hearing Aids Based on the Genetic Algorithm

  • Ying-Hui LaiEmail author
  • Chien-Hsun Chen
  • Shih-Tsang Tang
  • Zong-Mu Yeh
  • Yu Tsao
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

The noise reduction (NR) approach is a critical component for enabling hearing aid (HA) users to attain improved speech perception capabilities and to reduce the listening effort under noisy conditions. More specifically, the NR can improve the output signal-to-noise ratio (SNR) level to enhance the speech intelligibility and sound quality for individuals with hearing loss. Recently, a discriminative post-filter (DPF) approach has been developed and the preliminary results indicate that the DPF can further improve the output SNR performance. It is important to specify suitable parameters for the DPF; however, it remains unknown how to set these parameters properly. Therefore, this study mainly focuses on the genetic algorithm (GA) to investigate these parameters of the DPF to further enhance the output SNR performance for HA users. The results of the objective evaluations show that under noisy conditions, the parameters of the DPF investigated by the GA algorithm can maintain the sound quality and yield higher SNR performance as compared to the NR approach used solely. More specifically, the proposed method can enhance the output SNR over the NR approach applied singly by 5.31 dB and 4.05 dB at SNR conditions of 0 dB and 5 dB, respectively. It implies that the proposed approach can help HA users to reduce the listening effort and to hear well even in noisy conditions.

Keywords

Discriminative post filter Generalized Maximum A Posteriori Spectral Amplitude Genetic Algorithm Hearing Aids Noise Reduction 

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References

  1. 1.
    Organization, W.H., (2012) WHO global estimates on prevalence of hearing loss. Mortality and Burden of Diseases and Prevention of Blindness and Deafness, WHO.Google Scholar
  2. 2.
    Dillon, H., (2012) Hearing aids (2nd). Thieme.Google Scholar
  3. 3.
    Kochkin, S., (2010) MarkeTrak VIII: Consumer satisfaction with hearing aids is slowly increasing. The Hearing Journal. 63(1): p. 19-20.Google Scholar
  4. 4.
    Gnewikow, D., et al., (2009) Real-world benefit from directional microphone hearing aids. Journal of Rehabilitation Research and Development. 17: p. 29-33.Google Scholar
  5. 5.
    Wilson, K.W., et al., (2008) Speech denoising using nonnegative matrix factorization with priors. In ICASSP: p. 4029-4032.Google Scholar
  6. 6.
    Sigg, C.D., Dikk, T., and Buhmann, J.M., (2012) Speech enhancement using generative dictionary learning. Transactions on Audio, Speech, and Language Processing. 20(6): p. 1698-1712.Google Scholar
  7. 7.
    Lu, X., et al., (2013) Speech enhancement based on deep denoising autoencoder. In INTERSPEECH: p. 436-440.Google Scholar
  8. 8.
    Lu, X., et al., (2014) Ensemble modeling of denoising autoencoder for speech spectrum restoration. In INTERSPEECH: p. 885-889.Google Scholar
  9. 9.
    Xu, Y., et al., (2015) A regression approach to speech enhancement based on deep neural networks. IEEE Transactions on Audio, Speech, and Language Processing. 23(1): p. 7-19.Google Scholar
  10. 10.
    Xu, Y., et al., (2014) An experimental study on speech enhancement based on deep neural networks. IEEE Signal Processing Letters. 21(1): p. 65-68.Google Scholar
  11. 11.
    Ephraim, Y. and Malah, D., (1984) Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech and Signal Processing. 32(6): p. 1109-1121.Google Scholar
  12. 12.
    Lotter, T. and Vary, P., (2005) Speech enhancement by MAP spectral amplitude estimation using a super-Gaussian speech model. EURASIP Journal on Applied Signal Processing. (7) p. 1110-1126.Google Scholar
  13. 13.
    Fodor, B. and Fingscheidt, T., (2011) Speech enhancement using a joint map estimator with Gaussian mixture model for (non-) stationary noise. In ICASSP: p. 4768-4771.Google Scholar
  14. 14.
    Scalart, P., (1996) Speech enhancement based on a priori signal to noise estimation. In ICASSP: p. 629-632.Google Scholar
  15. 15.
    Tsao, Y., Y.C.S., Wu, J.E., and. Jean, F.R, (2013) Speech enhancement using generalized maximum a posteriori spectral amplitude estimator. In ICASSP: p. 7467-7471.Google Scholar
  16. 16.
    Lai, Y. H., et al., (2013) Evaluation of generalized maximum a posteriori spectral amplitude (GMAPA) speech enhancement algorithm in hearing aids. In Consumer Electronics (ISCE): p. 245-246.Google Scholar
  17. 17.
    Tsao, Y. and. Lai, Y.H., (2016) Generalized maximum a posteriori spectral amplitude estimation for speech enhancement. Speech Communication. (76):p.112-126.Google Scholar
  18. 18.
    Lai, Y.H., et al., (2013) Measuring the long-term SNRs of static and adaptive compression amplification techniques for speech in noise. Journal of the American Academy of Audiology. 24(8): p. 671-683.Google Scholar
  19. 19.
    Lai, Y.H., Tsao, Y., and Chen, F., (2013) A study of adaptive WDRC in hearing aids under noisy conditions. International Journal of Speech & Language Pathology and Audiology. 1(2): p. 43-51.Google Scholar
  20. 20.
    Lai, Y., et al., (2015) A discriminative post-filter for speech enhancement in hearing aids. In ICASSP: p. 5868-5872.Google Scholar
  21. 21.
    Holland, J.H., (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.Google Scholar
  22. 22.
    Golberg, D.E., (1989) Genetic algorithms in search, optimization, and machine learning. Addion Wesley.Google Scholar
  23. 23.
    Li, P. C., et al., (2005) Genetic algorithm for the efficient selection of disyllabic word lists used in Mandarin speech discrimination tests. Medical and Biological Engineering and Computing. 43(5): p. 648-657.Google Scholar
  24. 24.
    Wong, L.L., et al., (2007) Development of the Mandarin hearing in noise test (MHINT). Ear and Hearing. 28(2): p. 70-74.Google Scholar
  25. 25.
    Rix, A.W., et al., (2001) Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In ICASSP: p.749-752.Google Scholar
  26. 26.
    Chen, J., (2008) Fundamentals of noise reduction in spring handbook of speech processing. Springer.Google Scholar
  27. 27.
    Haykin, S., (1995) Advances in spectrum analysis and array processing (vol. III). Prentice-Hall, Inc.Google Scholar
  28. 28.
    Brons, I., Houben, R., and Dreschler, W.A., (2014) Effects of noise reduction on speech intelligibility, perceived listening effort, and personal preference in hearing-impaired listeners. Trends in Hearing. 18.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ying-Hui Lai
    • 1
    Email author
  • Chien-Hsun Chen
    • 2
  • Shih-Tsang Tang
    • 3
  • Zong-Mu Yeh
    • 2
  • Yu Tsao
    • 1
  1. 1.Research Center for Information Technology InnovationAcademia SinicaTaipeiTaiwan
  2. 2.Department of Mechatronic EngineeringNational Taiwan Normal UniversityTaipeiTaiwan
  3. 3.Department of Biomedical EngineeringMing Chuan UniversityTaoyuanTaiwan

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