  Auscultation provides a rich diversity of information to diagnose
cardiovascular and respiratory diseases. However, sound auscultation is
challenging due to noise. In this study, a modified version of the affine
non-negative matrix factorization (NMF) approach is proposed to blindly
separate lung and heart sounds recorded by a digital stethoscope. This method
applies a novel NMF algorithm, which embodies a parallel structure of
multilayer units on the input signal, to find a proper estimation of source
signals. Another key innovation is the use of the periodic property of the
signals which improves accuracy compared to previous works. The method is
tested on 100 cases. Each case consists of two synthesized mixtures of real
measurements. The effect of different parameters is discussed, and the results
are compared to other current methods. Results demonstrate improvements in the
source-to-distortion ratio (SDR), source-to-interference ratio (SIR), and
source-to-artifacts ratio (SAR) of heart and lung sounds, respectively.
