Hidden Markov model based respiratory sound classification

Abstract

© Springer Nature Singapore Pte Ltd. 2018. This paper presents a method based on hidden Markov models in combination with Gaussian mixture models for classification of respiratory sounds into normal, wheeze and crackle classes. Input features are mel-frequency cepstral coefficients extracted in the range between 50 Hz and 2000 Hz in combination with their first derivatives. The audio files are preprocessed to remove noise using spectral subtraction. Our best score achieved in the official ICHBI Challenge second evaluation phase is 39.56.