Lung Health Analysis: Adventitious Respiratory Sound Classification Using Filterbank Energies

Himadri Mukherjee, Hanan Salam, KC Santosh

Research output: Contribution to journalArticlepeer-review


Audio-based healthcare technologies are among the most significant applications of pattern recognition and Artificial Intelligence. Lately, a major chunk of the World population has been infected with serious respiratory diseases such as COVID-19. Early recognition of lung health abnormalities can facilitate early intervention, and decrease the mortality rate of the infected population. Research has shown that it is possible to automatically monitor lung health abnormalities through respiratory sounds. In this paper, we propose an approach that employs filter bank energy-based features and Random Forests to classify lung problem types from respiratory sounds. The adventitious sounds, crackles and wheezes appear distinct to the human ear. Moreover, different sounds are characterized by different frequency ranges that are dominant. The proposed approach attempts to distinguish the adventitious sounds (crackles and wheezes) by modeling the human auditory perception of these sounds. Specifically, we propose a respiratory sounds representation technique capable of modeling the dominant frequency range present in such sounds. On a publicly available dataset (ICBHI) of size 6898 cycles spanning over 5h, our results can be compared with the state-of-the-art results, in distinguishing two different types of adventitious sounds: crackles and wheezes.

Original languageEnglish (US)
Article number2157008
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number14
StatePublished - Nov 1 2021


  • Lung health
  • healthcare
  • respiratory infection
  • respiratory sound

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Lung Health Analysis: Adventitious Respiratory Sound Classification Using Filterbank Energies'. Together they form a unique fingerprint.

Cite this