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Computer analysis of Lung Sound (LS) signals has been proposed in recentyears as a tool to analyze the lungs' status but there have always been mainchallenges, including the contamination of LS with environmental noises, whichcome from different sources of unlike intensities. One of the common methods innoise reduction of LS signals is based on thresholding on Discrete WaveletTransform (DWT) coefficients or Empirical Mode Decomposition (EMD) of thesignal, however, in these methods, it is necessary to calculate the SNR valueto determine the appropriate threshold for noise removal. To solve thisproblem, a combined model based on EMD and Artificial Neural Network (ANN)trained with different SNRs (0, 5, 10, 15, and 20dB) is proposed in thisresearch. The model can denoise white and pink noises in the range of -2 to20dB without thresholding or even estimating SNR, and at the same time, keepthe main content of the LS signal well. The proposed method is also comparedwith the EMD-custom method, and the results obtained from the SNR, and fitcriteria 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 forwhite and pink noises, respectively, while the corresponding values arerespectively 5.89 and 4.31dB for the EMD-Custom method.

A Combined Model for Noise Reduction of Lung Sound Signals Based on Empirical Mode Decomposition and Artificial Neural Network