Lung sound classification using cepstral-based statistical features

• Published in: Computers in biology and medicine Vol. 75; pp. 118 - 129
• Main Authors: Sengupta, Nandini, Sahidullah, Md, Saha, Goutam
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2016; Elsevier Limited
• Subjects: Accuracy; Algorithms; Artificial neural network (ANN); Auscultation; Classification; Decomposition; Discrete wavelet transform (DWT); Humans; Internal Medicine; Mel-frequency cepstral coefficients (MFCCs); Neural Networks (Computer); Noise; Other; Respiratory Sounds - classification; Signal processing; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Sound; Spectral features; Statistical features; Mel-frequency cepstral coefficients (MFCCs); Discrete wavelet transform (DWT); Artificial neural network (ANN); Auscultation; Spectral features; Statistical features

Lung sounds convey useful information related to pulmonary pathology. In this paper, short-term spectral characteristics of lung sounds are studied to characterize the lung sounds for the identification of associated diseases. Motivated by the success of cepstral features in speech signal classifica...