  Computer analysis of Lung Sound (LS) signals has been proposed in recent
years as a tool to analyze the lungs' status but there have always been main
challenges, including the contamination of LS with environmental noises, which
come from different sources of unlike intensities. One of the common methods in
noise reduction of LS signals is based on thresholding on Discrete Wavelet
Transform (DWT) coefficients or Empirical Mode Decomposition (EMD) of the
signal, however, in these methods, it is necessary to calculate the SNR value
to determine the appropriate threshold for noise removal. To solve this
problem, a combined model based on EMD and Artificial Neural Network (ANN)
trained with different SNRs (0, 5, 10, 15, and 20dB) is proposed in this
research. The model can denoise white and pink noises in the range of -2 to
20dB without thresholding or even estimating SNR, and at the same time, keep
the main content of the LS signal well. The proposed method is also compared
with the EMD-custom method, and the results obtained from the SNR, and fit
criteria indicate the absolute superiority of the proposed method. For example,
at SNR = 0dB, the combined method can improve the SNR by 9.41 and 8.23dB for
white and pink noises, respectively, while the corresponding values are
respectively 5.89 and 4.31dB for the EMD-Custom method.
