Classification between normal and adventitious lung sounds using deep neural network

In this paper, a new classification method is proposed to distinguish between normal and adventitious lung sounds. Firstly, using a dual channel electronical stethoscope, the active noise control (ANC) approach is employed to enhance the lung sounds which are subject to noise contamination in practical environment. Secondly, hidden Markov model (HMM) is utilized to characterize normal or adventitious lung sounds, which adopts maximum likelihood approach to calculate the acoustic likelihood for each respiratory phase. Thirdly, a deep neutral network (DNN) estimates the posterior probability of HMM for each observation, instead of the traditional Gaussian mixture model (GMM). And the acoustic input features of DNN consist of Mel frequency cepstral coefficients (MFCCs) extracted from each frame of lung sound samples. To evaluate the proposed DNN-HMM framework for lung sound classification, the recognition rate is compared between proposed method and traditional GMM-HMM approach. The results indicate that the proposed classification method can significantly improve the classification performance.