  The bio-acoustic information contained within heart sound signals are
utilized by physicians world-wide for auscultation purpose. However, the heart
sounds are inherently susceptible to noise contamination. Various sources of
noises like lung sound, coughing, sneezing, and other background noises are
involved in such contamination. Such corruption of the heart sound signal often
leads to inconclusive or false diagnosis. To address this issue, we have
proposed a novel U-Net based deep neural network architecture for denoising of
phonocardiogram (PCG) signal in this paper. For the design, development and
validation of the proposed architecture, a novel approach of synthesizing
real-world noise corrupted PCG signals have been proposed. For the purpose, an
open-access real-world noise sample dataset and an open-access PCG dataset has
been utilized. The performance of the proposed denoising methodology has been
evaluated on the synthesized noisy PCG dataset. The performance of the proposed
algorithm has been compared with existing state-of-the-art (SoA) denoising
algorithms qualitatively and quantitatively. The proposed denoising technique
has shown improvement in performance as comparison to the SoAs.

Signal Processing;Sound;Audio and Speech Processing