Abstract
This study describes research developing a wavelet based, single microphone noise reduction algorithm for use in digital hearing aids. The approach reduces noise by expanding the observed speech in a series of implicitly filtered, shift-invariant wavelet packet basis vectors. The implicit filtering operation allows the method to reduce correlated noise while retaining low-level high-frequency spectral components that are necessary for intelligible speech. Recordings of speech in automobile road noise at signal-to-noise ratios of 0, 5, 10, 15, and 20 dB were used to evaluate the new method. Objective measurements indicate that the new method provides better noise reduction and lower signal distortion than previous wavelet-based methods, and produces output free from audible artifacts of conventional FFT methods. However, trials of the Revised Speech Perception in Noise test with the new algorithm showed no significant improvement in speech perception. Subsequent analysis has shown that the algorithm imposes physical attenuation on low-intensity components that mimics the perceptual effects of mild hearing loss.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Special Issue on diffusion maps and wavelets, Appl. Comput. Harmon. Anal. 21, (2006).
J. Berger, R. Coifman, M. Goldberg, Removing noise from music using local trigonometric bases and wavelet packets, Jour. Audio Eng. Soc., 808-818, (1994).
R. Bilger, J. Nuetzel, W. Rabinowitz and C. Rzeczowski, Standardization of a test of speech perception in noise, J. Speech Hear. Res., 27, 32-48 (1984).
S. Boll, Suppression of acoustic noise in speech using spectral subtraction, IEEE Trans. Accoustics, Speech, and Sig. Proc. 27, 113-120, (1979).
S. Boll, Speech enhancement in the 1980’s: noise suppression with patern matching. In S. Furui and M. Sondhi, editors, Advances in Speech Signal Processing, Marcel Dekker, 309-326.
G. Box and G. Jenkins, Time Series Analysis-Forecasting and Control, Holden Day, San Francisco, Ca., (1970).
J.C. Bremer, R.R. Coifman, M. Maggioni, A.D. Szlam, Diffusion wavele packets, Appl. Comput. Harmon. Anal. 21, 95-112, (2006).
R. Brey, M. Robinette, D. Chabries and R.W. Christiansen, Improvement in speech intelligibility in noise employing an adaptive filter with normal and hearing-impaired subjects, Jour. Rehab. Res. and Dev. 24, 75-86, (1987).
D. Byrne and H. Dillon, The National Acoustics Laboratories’ (NAL) new procedure for selecting the gain and frequency response of a hearing aid, Ear and Hearing, vol. 7, 257265, 1986.
D. Chabries, R.W. Christiansen, R. Brey, M. Robinette, R. Harris, Application of adaptive digital signal processing to speech inhancement for the hearing impaired, Jour. Rehab. Res. and Dev., 24 65-74, (1987).
H. Chipman, E. Kolaczyk and R. McCulloch, Signal de-noising using adaptive Bayesian wavelet shrinkage, Proc. IEEE-SP Intl. Symp. Time-Freq, Time Scale Anal., 225-228, (1996).
R. Coifman and D. Donoho, Translation-invariant de-noising. In A. Antoniadis, editor, Wavelets and Statistics, Springer-Verlag.
R. Coifman, M. Maggioni, Diffusion wavelets, Appl. Comput. Harmon. Anal. 21, 53-94, (2006).
R. Coifman and F. Majid, Adaptive waveform analysis and denoising. In Y. Meyer and S. Roques, editors, Progress in Wavelet Analysis and Applications, 63-76, (1993).
R. Coifman and N. Saito, Local discriminant bases and their applications, J. Math. Imag. Vision 5 (1995) 337-358.
R. Coifman and V. Wickerhauser, Entropy-based algorithms for best basis selection, IEEE Trans. Inf. Theory 38, (1992), 713-738.
I. Daubechies, Orthonormal bases of compactly supported wavelets, Comm. on Pure and Appl. Math. 4, (1988) 909-996.
D. Donoho, Unconditional bases are optimal bases for data compression and for statistical estimation, Appl. Comput. Harmonic Analysis 1, (1993) 100-115.
D. Donoho and I. Johnstone, Ideal spatial adaptation via wavelet shrinkage, Biometrika 81, 425-455, (1994).
D. Donoho, I. Johnstone and G. Kerkyacharian and D. Picard Wavelet Shrinkage: Asymptopia? J. Royal Stat. Soc. Ser. B. 2 301-337, (1995).
A.J. Duquesnoy and R. Plomp, The effect of a hearing aid on the speech-reception threshold of hearing-impaired listeners in quiet and in noise, Jour. Acoust. Soc. Amer., vol. 83, pp. 2166-2173, 1983.
L.A. Drake, J.C. Rutledge and J. Cohen, Wavelet Analysis in Recruitment of Loudness Compensation, IEEE Transactions on Signal Processing, Dec.1993.
C.W. Dunnett, A multiple comparison procedure for comparing several treatments to a control, Jour. Amer. Stat. Assoc., vol. 50, 1096-1121. 1955.
P. Duchnowski and P.M. Zurek, Villchur revisited: another look at automatic gain control of simulation of recuiting hearing loss, J. acoust. Soc. Amer. 98, 6, pp. 3170-3181 (1995).
Y. Ephraim and D. Malah, Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator, IEEE ATrans. Accoust. Speech Sig. Proc. 32, (1984) 1109-1122
Y. Ephraim, D. Malah and B. Juang, On the applications of hidden Markov models for enhancing noisy speech, IEEE ATrans. Accoust. Speech Sig. Proc. 32, (1984) 1846-1856.
Y. Ephraim and H.L. Van Trees, A signal subspace approach for speech enhancement, Proc. ICASSP 1993, volume 2, (1993) 355-358.
Y. Ephraim and H.L. Van Trees, A signal subspace approach for speech enhancement, IEEE Trans. Speech Audio Proc. 3, (1995) 251-266.
Y. Ephraim and H.L. Van Trees, M. Nilsson and S. Soli, Enhancement of noisy speech for the hearing impaired using the signal subspace approach. In Proc. NIH Interdisciplinary Forum on Hearing Aid Research and Development (1996).
D. Graupe, J. Grosspietsch, and S. Basseas, A single-microphone-based self-adaptive filter of noise from speech and its performance evaluation. Jour. Rehab. Res. Dev. 24, (1987) 119-126.
R. Gray, On the asymptotic eigenvalue distribution of Toeplitz matrices, IEEE Trans. Inf. Theory 18, (1972) 725-730.
J.P. Gagne, Excess masking among listeners with a sensorineural hearing loss, Jour. Acoust. Soc. Amer., vol. 81, pp. 2311-2321, 1988.
J. Greenberg and P. Zurek, Evaluation of an adaptive beamforming method for hearing aids, J. Acoust. Soc. Amer. 91, 1662-1676, (1992).
C.S. Hallpike and J.D. Hood, Observation upon the neurological mechanism of the loudness recruitment phenomenon, Acta Oto-Laryng. 50, pp. 472-486, (1959).
IEEE, IEEE recommended practice for speech quality measurements, IEEE Trans. Audio Electroacoustics, pp. 227-246, (1969).
A. Jansen and P. Niyogi, Intrinsic Fourier analysis on the manifold of speech sounds, Proc. International Conferene on Accoustics, Speech, and Signal Processing, Toulose, France, 2006.
M. Jansen, Noise reduction by wavelet thresholding, Springer Verlag, (2001).
J. Kates, Speech enhancement based on a sinusoidal model, J. Speech Hear. Res. 37, 449-464, (1994).
M.C. Killion, The K-Amp hearing aid: an attempt to present high fidelity for persons with impaired hearing, Amer. J. Audiology, pp. 52-74, (1993)
S. Kochkin, MarkeTrak III: Why 20 million in US do not use hearing aids for their hearing loss, Hearing J. 46, pp. 1-8, (1993).
S. Kochkin, MarkeTrak III: 10-year Customer satisfaction trends in the US Hearing Instrument Market, Hearing Rev. 9, pp. 1-8, (2002).
S. Kochkin, MarkeTrak VII: Customer satisfaction with hearing instruments in the digital age, Hearing J. 58, pp. 30-39, (2005).
H. Levitt, A historical perspective on digital hearing aids: how digital technology has changed modern hearing aids, Trends in Amplification 11, pp. 7-24, (2007).
M. Lang, H. Guo, J. Odegard, J. Burrus and R. Wells, Non-linear processing of a shift invariant DWT for noise reduction, IEEE Sig. Proc. Letters 3, 10-12, (1995).
H. Levitt, M. Bakke, J. kates, A. Neuman, T. Schwander and M. Weiss, Signal processing for hearing impairment, Scand. Audiol. Supplement 38, 7-19, (1993).
J. Lim, editor, Speech Enhancement, Prentice Hall, aenglewood Cliffs, N.J. (1983).
J. Lim and A. Oppenheim, All-pole modeling of degraded speech, IEEE Trans. Acoust. Speech Sig. Proc. 26, 197-209, (1978).
H. Levitt and S.B. Resnick, Speech reception by the hearing impaired: methods of testing and the development of new tests, Scand. Audiol. Supp. 6, pp. 107-130, (1978).
J. Makhoul and R. McAulay, Removal of noise from noise-degraded speech, Technical report, National Academy of Sciences, National Academy Press, Washington, D.C. (1989).
S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. and Machine Intell. 11, 674-693, (1989).
R. McAulay and M. Malpass, Speech enhancement using a soft-decision noise suppression filter, IEEE Trans. Acoust. Speech Sig. Proc. 28, 137-145, (1980).
E.S. Martin and J.M. Pickett, Sensironeural hearing loss and upward spread of masking, Jour. Speech Hear. Res., vol. 13, pp. 426-437, 1970.
J.C. Pesquet, H. Krim and H. Carfantan, Time-invariant orthonormal wavelet representations, IEEE Trans. Sig. Proc. 44, (1996) 1964-1970.
P. Peterson, N. Durlach, W. Rabinowitz and P. Zurek, Multimicrophone adaptive beamforming for interference reduction in hearing aids, J. Rehab. Res. Dev. 24, 102-110, (1987).
J. Porter and S. Boll, Optimal estimators for spectral resoration of noisy speech. In Proc. ICASSP 1984, volume 1, page 18A.2.
S. Quackenbush, T. Barnwell and M. Clements, Objective measures of Speech Quality, Prentice Hall, Englewood Cliffs, N.J. (1993).
J.C. Rutledge, Speech Enhancement for Hearing Aids, in Time-Frequency and Wavelet Transforms in Biomedicine, Metin Akay, Editor, IEEE Press, 1997.
J. Rissanen, Modeling by shortest data description, Automatica 14, 465-471, (1978).
T. Roos, P. Myllymaki and J. Rissanen, MDL Denoising Revisited, IEEE Transactions on Signal Processing, vol. 57, No. 9, 3347-3360, (2009).
N. Saito, Simultaneous noise suppression and signal compression using a library of orthonormal and signal compression using a library of orthonormal bases and the minimum description length criterion. In E. Foufoula-Georgiou and P. Kumar, editors, Wavelets in Geophysics, academic Press (1994).
C. Sammeth and M. Ochs, A review of current “noise reduction” hearing aids: rationale, assumptions and efficacy, Ear and Hearing 12, 116S-124S (1991).
T. Schwander and H. Levitt, Effect of two-microphone noise reduction on speech recognition by normal-hearing listeners, Jour. rehab. res. and Dev. 24, (1987) 87-92.
R. Schwartz, M. Berouti and J. Makhoul, Enhancement of speech corrupted by acoustic noise, ICASSP-79 Proceedings, page 208 (1979).
D. Sinha and A. Tewfik, Low bit rate transparent audio compression using adapted wavelets, IEEE Trans. Sig. Proc. 41, 3463-3479, (1993).
M. Smith and T. Barnwell, Exact reconstruction techniques for tree-structured subband coders, IEEE Trans. Acoust.Sig. Proc. 34, 434-441, (1986).
C. Stein, Estimation of the mean of a multivariate normal distribution, Ann. Stat. 9, 6, pp. 1135-1151, (1981).
R. Tyler and F. Kuk, The effects of “noise suppression” hearing aids on sonsonant recognition in speech-babble and low-frequency noise,. Ear and hearing 10, 243-249, (1989).
A.R. Thornton and M.J.M. Raffin, Speech discrimination scores modeled as a binomial variable, Jour. Speech Hear. Res., vol. 21, pp. 507-518, 1978.
B. Vidakovic, Non-linear wavelet shrinkage with Bayes rules and Bayes factors, Discussion Paper 94-24, Duke University (1994).
J. Verschure and P.P.G. Van Benthem, Effect of hearing aids on speech perception in noisy situations, Audiology, vol. 31, pp. 205-221, 1992.
W. Voiers, Diagnostic acceptability measure for speech communication systems. In Proc. ICASSP 1977, pages 204-207, (1977).
S. Watanabe, Karhunen-Loève expansion and factor analysis: Theoretical remarks and applications, Trans. 4th Prague Conf. Inform. Theory, Statist. Decision Functions, rand. Proc., 635-660, Prague Publishing House of the czechoslovak Academy of Sciences, (1967).
N. Whitmal, Wavelet-Based Noise reduction for Speech Enhancement, PhD thesis, Northwestern University, Evanston, Il. (1997).
N. Whitmal, J. Rutledge and J. Cohen, Reducing correlated noise in digital hearing aids, IEEE Eng. Med. Biol. Mag. 15, 88-96, (1996).
N. Whitmal, J. Rutledge and J. Cohen, Reduction of autoregressive noise with shift-invariant wavelet packets, Proc. IEEE-SP Symp. Time-Freq. Time-Scale Analysis, pp. 137-140, (1996).
G. Wornell (1990). A Karhunen-Loève-like expansion for 1/f processes via wavelets, IEEE Trans. Inf. Theory 36, 859-861, (1990).
N. Whitmal and A. Vosoughi, Recruitment of loudness effects of attenuative noise reduction algorithms, J. Acoust. Soc. Amer. vol. 111, Issue 5, p. 2380, (2002), (Conference Proceedings).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Whitmal, N., Rutledge, J., Cohen, J. (2011). Denoising Speech Signals for Digital Hearing Aids: A Wavelet Based Approach. In: Cohen, J., Zayed, A. (eds) Wavelets and Multiscale Analysis. Applied and Numerical Harmonic Analysis. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-8095-4_14
Download citation
DOI: https://doi.org/10.1007/978-0-8176-8095-4_14
Published:
Publisher Name: Birkhäuser Boston
Print ISBN: 978-0-8176-8094-7
Online ISBN: 978-0-8176-8095-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)