Segmentation of the phonocardiography (PCG) signal into cardiac cycles is a primary task for the diagnosis of cardiovascular diseases. However, PCG is highly susceptible to noise, and extra sound called murmur may also be present in the PCG signal due to pathology. These components cause difficulties in the segmentation and therefore, segmentation is often preceded by the denoising of the PCG signal to emphasize the fundamental heart sounds S1 and S2, by removing these unwanted components. For the denoising of the PCG signal, discrete wavelet transform (DWT) based algorithms have shown good performance because such algorithms suppress in-band noise besides the out-of-band noise. Selection of threshold value and threshold function significantly affects the performance of these algorithms. In this paper, for threshold value estimation, an adaptive method based on statistical parameters of the given PCG signal is proposed. The statistical parameters are found to be highly effective for this purpose. We also propose a new threshold function, non-linear mid function, to address the issues of SNR and transients in the existing threshold functions, soft and hard. The proposed method is applied on a large number of PCG signals with additive white Gaussian noise, red noise, and pink noise. The Performance of the proposed method is also evaluated on the PCG signals recorded in real-life noisy scenarios and signals with murmur sound. The obtained results show that the proposed method is significantly superior to the competitive algorithms. © 2017 Elsevier Ltd