  Lung sounds refer to the sound generated by air moving through the
respiratory system. These sounds, as most biomedical signals, are non-linear
and non-stationary. A vital part of using the lung sound for disease detection
is discrimination between normal lung sound and abnormal lung sound. In this
paper, several approaches for classifying between no-crackle and crackle lung
sounds are explored. Decomposition methods such as Empirical Mode
Decomposition, Ensemble Empirical Mode Decomposition, and Discrete Wavelet
Transform are used along with several feature extraction techniques like
Principal Component Analysis and Autoencoder, to explore how various
classifiers perform for the given task. An open-source dataset downloaded from
Kaggle, containing chest auscultation of varying quality is used to determine
the results of using the different decomposition and feature extraction
combinations. It is found that when higher-order statistical and spectral
features along with the Mel-frequency cepstral coefficients are fed to the
classier we get the best performance with the kNN classifier giving the best
accuracy. Furthermore, it is also demonstrated that using a combination of
feature selection methods one can significantly reduce the number of input
features without adversely affecting the accuracy of the classifiers.

Sound;Audio and Speech Processing