BionicWavelet Based Denoising Using Source Separation
Keywords:Bionic wavelet transform, Blinde Source Separation, entropy, speech enhancement
We consider the problem of speech denoising using source separation. In this study we have proposed a hybrid technique that consists in applying in the first step, the Bionic Wavelet Transform (BWT) to two different mixtures of the same speech signal with noise. This speech signal is corrupted by a Gaussian white noise with two different values of the Signal to Noise Ratio (SNR) in order to obtain those two mixtures. The second step consists in computing the entropy of each bionic wavelet coefficient and finds the two subbands having the minimal entropy. Those two subbands are used to estimate the separation matrix of the speech signal from noise by using the source separation. Our proposed technique is evaluated by comparing it to the denoising technique based on source separation in time domain.
A. J. Bell, T. J. Sejnowski, An information maximization approach to blind separation and blind econvolution, Neural Computation, Vol.7, pp.1004-1034, 1995. http://dx.doi.org/10.1162/neco.19184.108.40.2069
A Hyvarinen, J. Karhunen, E. Oja, Independent component analysis, Wiley and Sons, 2001. http://dx.doi.org/10.1002/0471221317
T. Tanaka, A. Cichocki, Subband decomposition independent component analysis and new performance riteria, ICASSP, pp.541-544, 2004.
P. Kisilev, M. Zibulevsky, Blind source separation using multinode sparse representation, ICIP, 2001.
R. Moussaoui, J. Rouat, R. Lefebvre, Wavelet Based Independent Component Analysis for Multi- hannel Source Separation, ICASSP, pp.645-648, 2006.
J. Yao, Y. T. Zhang, Bionic wavelet transform: a new timefrequency method based on an auditory odel, IEEE Trans. on Biomedical Engineering Vol.48, No.8, pp.856-863, 2001. http://dx.doi.org/10.1109/10.936362
Xiaolong Yuan, B.S.E.E. A THESIS, Ë‡T Auditory Model-based BionicWavelet Transform for speech nhancement. Electrical and computer engineering.
M. T. Johnsona, X. Yuanb, Y. Rena, Speech signal enhancement through adaptive wavelet thresholding. n conference Elsevier, pp.123-133, 2007.
J. Yao, Y. T. Zhang, The application of bionic wavelet transform to speech signal processing in ochlear implants using neural network simulations, IEEE Trans. Biomed. Eng., Vol.49, No.11, pp. 299-1309, 2002.
B. Chen, P. C. Loizou, A Laplacian-based MMSE estimator for speech enhancement, Speech Communication, ol.49, No.2, pp.134-143, 2007.
ITU-T P.862. Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end peech quality assessment of narrowband telephone networks and speech codecs, ITU Recommendation .862, 2001.
A. W. Rix, J. G. Beerends, M. P. Hollier, A. P. Hekstra, Perceptual evaluation of speech quality (pesq) - a new method for speech quality assessment of telephone networks and codecs, ICASSP, p.749-752, 2001.
Y. Hu, P. C. Loizou, Evaluation of objective measures for speech enhancement, IEEE Trans. Speech, udio Processing, Vol.16, No.1, pp.229-238, 2008.
E. Zavarehei, S. Vaseghi, Q. Yan. Inter- frame modeling of DFT trajectories of speech and noise for peech enhancement using Kalman filters, Speech Communication, Vol.48, No.11, pp.1545-1555, 006.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.