Blind Source Separation of Noisy Mixed Speech Signals

Hui Ya Li; Jian Ying Shi; Jin Xi Men

doi:10.4028/www.scientific.net/AMM.475-476.291

Paper Titles

The Difference Effect of Wavelet Transforms and Gaussian Filter in Multi-Resolution Optical Flow
p.273

Impact of Missing Data on Parameter Estimation of EM Algorithm under Rayleigh Distribution
p.278

Noise Analysis and Filtering for Laser Active Imaging System
p.282

Analysis and Study on GNSS Data under EHV Transmission Environment by Using TEQC
p.287

Blind Source Separation of Noisy Mixed Speech Signals
p.291

A New 3D Coordinates Acquisition Method on Base of Single Camera
p.296

Method for Testability Demonstration Test Based on Cost Optimization
p.301

Cloud Computing Model for Big Geological Data Processing
p.306

Membership Calculation Based on Dimension Hierarchical Division
p.312

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 475-476Blind Source Separation of Noisy Mixed Speech...

Blind Source Separation of Noisy Mixed Speech Signals

Abstract:

In this paper, a new method for blind source separation of the noisy mixed speech signals is introduced. Firstly, the adaptive spectral subtraction is adopted to eliminate noise of noisy mixed speech signals. Secondly, the FASTICA algorithm is used to separate denoised mixed speech signals .Finally, wavelet transform is applied to remove the residual noise, and then the estimation of each speech source signal can be got.

You might also be interested in these eBooks

Sensors, Measurement and Intelligent Materials II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 475-476)

Pages:

291-295

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.475-476.291

Citation:

Cite this paper

Online since:

December 2013

Authors:

Hui Ya Li, Jian Ying Shi, Jin Xi Men

Keywords:

Adaptive Spectral Subtraction, Blind Source Separation, Independent Component Analysis (ICA), Noisy Mixed Speech Signals, Wavelet Denoising

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] S. Haykin, Zhe Chen. The cocktail party problem: Neural Computation. Vol. 17(9), p.1875–1902, (2005).

DOI: 10.1162/0899766054322964

Google Scholar

[2] H. Le Borgne, A, Guérin-Dugué, A. Antoniadis. Representation of images for classification with independent features: Pattern Recognition Letters. Vol. 25(2), p.141–154, (2004).

DOI: 10.1016/j.patrec.2003.09.011

Google Scholar

[3] W. Wang , J. A. Chambers, S. Sanei. A novel hybrid approach to the permutation problem of frequency domain bind source separation: ICA 2004 LNCS 3195, p.532–539, (2004).

DOI: 10.1007/978-3-540-30110-3_68

Google Scholar

[4] Yau, R. Macroeconomic forecasting with independent component analysis: Econometric Society 2004 Far Eastern Meetings, Econometric Society. Vol. (741), (2004).

Google Scholar

[5] A. Hyvärinen , E. Oja, A fast fixed-point algorithm for independent component analysis: Neural computation, Vol. 9(7), pp.1483-1492, (1997).

DOI: 10.1162/neco.1997.9.7.1483

Google Scholar

[6] M. Zibulevsky, P. Kisilev, Y. Y. Zeevi& B. A. Pearlmutter. Blind source separation via multinode sparse representation. In Advances in neural information processing systems, pp.1049-1056, (2001).

DOI: 10.1109/icip.2001.958086

Google Scholar

[7] O. Yilmaz , S. Rickard. Blind separation of speech mixtures via time-frequency masking: IEEE Trans. Sig. Proc., Vol. 52(7), p.1830–1847, (2004).

DOI: 10.1109/tsp.2004.828896

Google Scholar

[8] M. I. Mandel, S. Bressler, B. Shinn-Cunningham & D. P. W. Ellis. Evaluating source separation algorithms with reverberant speech: IEEE Transactions on audio speech and language processing, Vol. 18(7), pp.1872-1883, (2010).

DOI: 10.1109/tasl.2010.2052252

Google Scholar

[9] S. K. Basha, P. C. Pandey. Real time enhancement of electrolaryngeal speech by spectral subtraction: Proc. 18th national conference on communications, pp.516-520, (2012).

DOI: 10.1109/ncc.2012.6176807

Google Scholar

[10] S. M. Hariharan, S. Kamal & S. Pillai. Reduction of self-noise effects in onboard acoustic receivers of vessels using spectral subtraction: Acoustics 2012 Nantes. pp.3793-3798, (2012).

Google Scholar

[11] D. Sodoyer, B. Rivet, L. Schwartz, J. L. Schwartz & C. Jutten. An analysis of visual speech information applied to voice activity detection. IEEE International Conference on Acoustics, Speech and Signal Processing. Vol(1), pp.1-1, (2006).

DOI: 10.1109/icassp.2006.1660092

Google Scholar