  Lung sounds contain vital information about pulmonary pathology. In this
paper, we use short-term spectral characteristics of lung sounds to recognize
associated diseases. Motivated by the success of auditory perception based
techniques in speech signal classification, we represent time-frequency
information of lung sounds using mel-scale warped spectral coefficients, called
here as mel-frequency spectral coefficients (MFSCs). Next, we employ local
binary pattern analysis (LBP) to capture texture information of the MFSCs, and
the feature vectors are subsequently derived using histogram representation.
The proposed features are used with three well-known classifiers in this field:
k-nearest neighbor (kNN), artificial neural network (ANN), and support vector
machine (SVM). Also, the performance was tested with multiple SVM kernels. We
conduct extensive performance evaluation experiments using two databases which
include normal and adventitious sounds. Results show that the proposed features
with SVM and also with kNN classifier outperform commonly used wavelet-based
features as well as our previously investigated mel-frequency cepstral
coefficients (MFCCs) based statistical features, specifically in abnormal sound
detection. Proposed features also yield better results than morphological
features and energy features computed from rational dilation wavelet
coefficients. The Bhattacharyya kernel performs considerably better than other
kernels. Further, we optimize the configuration of the proposed feature
extraction algorithm. Finally, we have applied mRMR (minimum-redundancy
maximum-relevancy) based feature selection method to remove redundancy in the
feature vector which makes the proposed method computationally more efficient
without any degradation in the performance. The overall performance gain is up
to 24.5% as compared to the standard wavelet feature based system.
