This paper focuses on the denoising of phonocardiogram (PCG) signals by means of discrete wavelet transform (DWT) using different wavelets and noise level estimation methods. The signal obtained by denoising from PCG signal contaminated white noise and the original PCG signal is compared to determine the appropriate parameters for denoising. The comparison is evaluated in terms of signal to noise ratio (SNR) before and after denoising. The results showed that the decomposition level is the most important parameter determining the denoising quality.
 M. Akay, J. L. Semmlow, W. Welkowitz, M.D. Bauer and J. B. Kostis, “Detection of Coronnary Occlusion Using Autoregressive Modeling Diastolic Heart Sounds,” IEEE Transactions on Biomedical Engineering, Vol. 37, 1990, pp. 336-273.doi:10.1109/10.52343
 L.G. Durand, M. Blanchard, G. Cloutier, H. N. Sabbah and P.D. Stein, “Comparison of Pattern Recognition Methods for Computer-Assisted Classification of Spectra of Heart Sounds in Patient with Porcine Bioprosthetic Valve Implanted in the Mitral Position,” IEEE Transactions on Biomedical Engineering, Vol. 37, 1990, pp. 1121-1129.doi:10.1109/10.64456
 A. Effern, K. Lehnertz, T. Schreiber, T. Grundwald, P. David and C. E. Elger, “Nonlinear Denoising of Transient Signals with Application to Event-related Potentials,” Physica, Vol. 140, 2000, pp. 257-266.