A Contourlet Based Block Matching Process for Effective Audio Denoising
(*) Corresponding author
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Audio signals are frequently infected by background environment noise and buzzing or whining noise from audio equipments. Audio denoising is the technique intends to satisfy the noise as retaining the fundamental signals. For the elimination of noises from the digital audio signals more number of denoising techniques is introduced by the researchers. However the effectiveness of those techniques remains a problem. In this paper, an audio denoising technique based on contourlet transformation is proposed. The contoulet transform which is more efficient in finding and removing the noises. Denoising is carried out in the transformation domain and the enhancement in denoising is attained by a process of grouping closer blocks and creation of multidimensional arrays. In this technique each and every finest detail supplied by the grouped set of blocks and also it protects the important unique features every separate block. Every block are filtered and restored in their original positions from where they are separated. The grouped blocks overlap each other and therefore for every element a much different assessment is obtained that are to be combined to remove noise from the input signal. The experimental result shows that the proposed Contourlet and the Daubechies’s transformation is more efecient when compared with the other techniques.
Copyright © 2013 Praise Worthy Prize - All rights reserved.
L. Breiman, J. Friedman, R. Olshen, and C.J Stone, Classification and Regression Trees, Belmont, CA:Wadsworth, 1983.
R. J. McAulay and M. L. Malpass, “Speech enhancement using soft decision noise suppression filter,” IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-28, no. 2, pp. 137–145, Apr. 1980.
M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), 1979, vol. 4, pp. 208–211.
S. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Trans. Acoustics, Speech, Signal Process., vol. ASSP-27, no. 2, pp. 113–120, Apr. 1979.
J. S. Lim and A. V. Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proc. IEEE, vol. 67, Dec. 1979.
D. Donoho and I. Johnstone, “Idea spatial adaptation via wavelet shrinkage,” Biometrika, vol. 81, pp. 425–455, 1994.
O. Cappé, “Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor,” IEEE Trans. Speech, Audio Process., vol. 2, pp. 345–349, Apr. 1994.
G. Yu, E. Bacry, and S. Mallat, “Audio signal denoising with complex wavelets and adaptive block attenuation,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Apr. 2007, vol. 3, pp. III-869–III-872.
Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error short-time spectral amplitude estimator,” IEEE. Trans. Acoust., Speech, Signal Process., vol. 32, no. 6, pp. 1109–1121, Dec. 1984.
Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error log-spectral amplitude estimator,” IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-33, no. 2, pp. 443–445,Apr. 1985.
D.L. Donoho, I.M. Johnstone. “ Ideal Denoising in an Orthonormal Basis Chosen from a Library of Bases” Technical report, vol. 461, Department of Statistics, Stanford University, 1994.
Donoho, D. L. and Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. J. Amer. Statist. Assoc. 90 1200--1224.
Bahoura, M., Rouat, J., 2001. Wavelet speech enhancement based on the teager energy operator. IEEE Signal Process. Lett. 8 (1), 10–12.
Ching-Ta and Hsiao-Chuan Wang, 2003. "Enhancement of single channel speech based on masking property and wavelet transform", Speech Communication, Vol. 41, No 2-3, pp: 409-427.
Dolby, “Making Cassettes Sound Better,” http://- www.dolby.com/cassette/bcsnr/, 2000.
Chen, S.-H., Chau, S.Y., Want, J.-F., 2004. Speech enhancement using perceptual wavelet packet decomposition and teager energy operator. J. VLSI Signal Process. Systems 36 (2–3), 125–139.
Jianzhao Huang, Jian Xie, Qinhe Gao, Liang Li, A Signal Threshold Denoising Method Based on Improved EEMD, (2012) International Review on Computers and Software (IRECOS), 7 (7), pp. 3600-3604.
Fu, Q., Wan, E.A., 2003. Perceptual wavelet adaptive denoising of speech. Paper presented at the Eurospeech, Geneva.
Hu, Y., Loizou, P.C., 2004. Speech enhancement based on wavelet thresholding the multitaper spectrum. IEEE Trans. Speech Audio Process. 12 (1), 59–67.
Lu, C.-T., Wang, H.-C., 2003. Enhancement of single channel speech based on masking property and wavelet transform. Speech Commun. 41 (2–3), 409–427.
Rekha Lakshmanan and Vinu Thomas,” Microcalcification Detection by Morphology,Singularities of Contourlet Transform and Neural Network”, Bonfring International Journal of Networking Technologies and Applications, Vol. 1, No. 1, 2012
M. H. Malik, S. A. M. Gilani, Anwaar-ul-Haq, Adaptive Image Fusion Scheme Based on Contourlet Transform and Machine Learning, (2008) International Review on Computers and Software (IRECOS) 3 (1), pp. 62-69.
Xuelong Hu, Wei Fang, Wanpei Chen, Tongyu Jiang, Canjun Qian, Mean-Shift Tracking Algorithm Based on Fused Texture Feature of Contourlet Transform, (2012) International Review on Computers and Software (IRECOS), 7 (7), pp. 3502-3506.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize