Noise Estimation and Reduction in Heart Sounds Using Time Frequency Block Thresholding Method
DOI:
https://doi.org/10.51983/ajes-2016.5.1.1968Keywords:
Block thresholding, Activity detection, soft thresholding, overlapping group shrinkageAbstract
In this paper a novel method of de-noising phonocardiogram by time-frequency Overlapping Group Shrinkage method is described. In this method sigma, the standard deviation of the stationary noise present in a noisy phonocardiogram is found using activity detection. This noise is then canceled by attenuating it in the time frequency domain. The accuracy of noise reduction is measured by SNR. Overlapping Group shrinkage algorithm reduces the effect of noise by attenuating them using hard or soft thresholding. Performance of this method was found to be far better compared to other methods such as Soft Thresholding and Block Thresholding.
References
P. J. Bentley, "Heart challenge," [Online]. Available: http://www.peterjbentley.com/heartchallenge/.
B. Lee, Hewlett-Packard Laboratories, Palo Alto, CA, USA, and M. Hasegawa-Johnson, University of Illinois at Urbana-Champaign, Urbana, IL, USA, "Minimum mean-squared error a posteriori estimation of high variance vehicular noise."
G. Yu, S. Mallat, and E. Bacry, "Audio denoising by time-frequency block thresholding," IEEE Transactions on Signal Processing, vol. 56, no. 5, pp. 2345-2357, May 2008.
J. Ramírez, J. C. Segura, C. Benítez, L. García, and A. Rubio, "Statistical voice activity detection using a multiple observation likelihood ratio test," IEEE Signal Processing Letters, vol. 12, no. 10, pp. 725-728, Oct. 2005.
E. Martin, M. de Masson d'Autume, and C. Varray, "Audio denoising algorithm with block thresholding," Image Processing On Line, vol. 2, pp. 85-92, Jul. 2012.
P.-Y. Chen and I. W. Selesnick, "Translation-invariant shrinkage/thresholding of group sparse signals," Polytechnic Institute of New York University, Brooklyn, NY, USA.
J. Sohn and W. Sung, "A voice activity detector employing soft decision based noise spectrum adaptation," in Proc. Int. Conf. Acoust., Speech, and Sig. Process., 1998, pp. 365-368.
D. L. Donoho, "De-noising by soft-thresholding," IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627, May 1995.
M. Figueiredo and R. Nowak, "Wavelet-based image estimation: An empirical Bayes approach using Jeffrey's noninformative prior," IEEE Transactions on Image Processing, vol. 10, no. 9, pp. 1322-1331, Sep. 2001.
H. Gao, "Wavelet shrinkage denoising using the nonnegative garrote," Journal of Computational and Graphical Statistics, vol. 7, pp. 469-488, 1998.
J. M. Fadili and L. Boubchir, "Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities," IEEE Transactions on Image Processing, vol. 14, no. 2, pp. 231-240, Feb. 2005.
A. Hyvärinen, "Sparse code shrinkage: Denoising of non-Gaussian data by maximum likelihood estimation," Neural Computation, vol. 11, pp. 1739-1768, 1999.
S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, Jul. 1989.
S. 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, Apr. 1979.
M. Berouti, R. Schwartz, and J. Makhoul, "Enhancement of speech corrupted by acoustic noise," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Apr. 1979, vol. 4, pp. 208-211.
S. Ghael, A. M. Sayeed, and R. G. Baraniuk, "Improved wavelet denoising via empirical Wiener filtering," in Proc. SPIE Tech. Conf. Wavelet Appl. Signal Proc., Jul. 1997, San Diego, CA, USA.
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