| |
| |
|
|
|
|
| Single-Channel Speech Enhancement by NWNS and EMD
|
|
Full
text: |
 PDF(329.7KB) |
|
|
Source |
Signal Processing: An International Journal (SPIJ) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(3.92MB) |
|
Volume: 4 Issue: 5 |
| |
Pages: 247-303 |
|
Publication
Date: December 2010 |
|
ISSN
(Online): 1985-2339 |
|
|
|
|
|
Pages |
279 - 291 |
|
Author(s) |
|
|
|
Published
Date |
20-12-2010 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: speech enhancement, non linear weighted noise subtraction, empirical mode decomposition |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. refSeek |
| 2. Academic Index |
| 3. Socol@r |
| 4. iSEEK |
| 5. Scribd |
| 6. PDFCAST |
| 7. Directory of Open Access Journals (DOAJ) |
| 8. Docstoc |
| |
|
| |
|
|
| This paper presents the problem of noise reduction from observed speech by means of improving quality and/or intelligibility of the speech using single-channel speech enhancement method. In this study, we propose two approaches for speech enhancement. One is based on traditional Fourier transform using the strategy of Noise Subtraction (NS) that is equivalent to Spectral Subtraction (SS) and the other is based on the Empirical Mode Decomposition (EMD) using the strategy of adaptive thresholding. First of all, the two different methods are implemented individually and observe that, both the methods are noise dependent and capable to enhance speech signal to a certain limit. Moreover, traditional NS generates unwanted residual noise as well. We implement nonlinear weight to eliminate this effect and propose Nonlinear Weighted Noise Subtraction (NWNS) method. In first stage, we estimate the noise and then calculate the Degree Of Noise (DON1) from the ratio of the estimated noise power to the observed speech power in frame basis for different input Signal-to-Noise-Ratio (SNR) of the given speech signal. The noise is not accurately estimated using Minima Value Sequence (MVS). So the noise estimation accuracy is improved by adopting DON1 into MVS. The first stage performs well for wideband stationary noises and performed well over wide range of SNRs. Most of the real world noise is narrowband non-stationary and EMD is a powerful tool for analyzing non-linear and non-stationary signals like speech. EMD decomposes any signals into a finite number of band limited signals called intrinsic mode function (IMFs). Since the IMFs having different noise and speech energy distribution, hence each IMF has a different noise and speech variance. These variances change for different IMFs. Therefore an adaptive threshold function is used, which is changed with newly computed variances for each IMF. In the adaptive threshold function, adaptation factor is the ratio of the square root of added noise variance to the square root of estimated noise variance. It is experimentally observed that the better speech enhancement performance is achieved for optimum adaptation factor. We tested the speech enhancement performance using only EMD based adaptive thresholding method and obtained the outcome only up to a certain limit. Therefore, further enhancement from the individual one, we propose two-stage processing technique, NWNS+EMD. The first stage is used as a pre-process for noise removal to a certain level resulting first enhanced speech and placed this into second stage for further removal of remaining noise as well as musical noise to obtain final enhancement of the speech. But traditional NS in the first stage produces better output SNR up to 10 dB input SNR. Furthermore, there are musical noise and distortion presented in the enhanced speech based on spectrograms and waveforms analysis and also from informal listening test. We use white, pink and high frequency channel noises in order to show the performance of the proposed NWNS+EMD algorithm. |
| |
|
| |
|
| |
| 1 |
M. E. Hamid, K. Ogawa, and T. Fukabayashi, “Improved Single-channel Noise Reduction Method of Speech by Blind Source Separation”, Acoust. Sci. & Tech., Japan, 28(3):153-164, 2007
|
|
|
| 2 |
J. Benesty, S. Makino, and J. Chen, “Speech Enhancement”, Springer-Verlag Berlin Heidelberg, 2005
|
|
|
| 3 |
M. M. Sondhi, C. E. Schmidt and L. R. Rabiner, “Improving the Quality of a Noisy Speech Signal”, Bell Syst. Techn. J., vol. 60, October 1981
|
|
|
| 4 |
S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 27, no. 2, pp. 113-120, April 1979
|
|
|
| 5 |
R. Martin, “Spectral Subtraction Based on Minimum Statistics”, Proc. EUSIPCO, pp. 1182- 1185, 1994
|
|
|
| 6 |
R. Martin, “Speech Enhancement based on Minimum Mean-Square Error Estimation and Supergaussian Priors”, IEEE Trans. Speech and Audio Process., vol. 13, no. 5, pp. 845-858, Sept. 2005
|
|
|
| 7 |
C. He, and G. Zweig, “Adaptive two-band spectral subtraction with multi-window spectral estimation”, ICASSP, vol. 2, pp. 793-796, 1999
|
|
|
| 8 |
S. C. Liu, “An approach to time-varying spectral analysis”, J. EM. Div. ASCE 98, 245-253, 1973
|
|
|
| 9 |
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shin, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The Empirical Mode Decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, Proceeding Royal Society London A, vol. 454, pp. 903-995, 1998
|
|
|
| 10 |
S. F. Boll, and D. C. Pulsipher, “Suppression of Acoustic Noise in Speech using TwoMicrophone Adaptive Noise Cancellation”, Correspondence, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-28, no. 6, pp. 752-753, Dec 1980
|
|
|
| 11 |
Z. Xiaojie, L. Xueyao, Z. Rabu, “Speech Enhancement Based on Hilbert-Huang Transform Theory”, in First International Multi-Symposiums on Computer and Computational Sciences, pp. 208-213, 2006
|
|
|
| 12 |
P. Flandrin, P. Goncalves and G. Rilling, “Detrending and Denoising with Empirical Mode Decompositions”, In Proc., EUSIPCO, pp.1581-1584, 2004
|
|
|
| 13 |
K. Khaldi, A. O. Boudraa, A. Bouchikhi, and M. T. H. Alouane, “Speech Enhancement via EMD”, in EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 873204, 8 pages, 2008
|
|
|
| 14 |
T. Hasan, and M. K. Hasan, “Suppression of Residual Noise from Speech Signals using Empirical Mode Decomposition”, Signal Processing Letters, IEEE, vol. 16, no. 1, pp. 2- 5, Jan 2009
|
|
|
| 15 |
X. Zou, X. Li, and R. Zhang, “Speech Enhancement Based on Hilbert-Huang Transform Theory”, First International Multi-Symposiums on Computer and Computational Sciences, 1: 208–213, 2006
|
|
|
| 16 |
Flandrin, P., Rilling, G. and Goncalves, P., "Empirical mode decomposition as a filter bank," IEEE Signal Processing Letters, 11(2), pp. 112-114, 2004
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| Somlal Das : Colleagues
|
|
| Mohammad Ekramul Hamid : Colleagues
|
|
| Keikichi Hirose : Colleagues
|
|
| Md. Khademul Islam Molla : Colleagues
|
|
|
|
|
|
|
|
|
|
|
