Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be d...
| Main Authors: | , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
IEEE
2018
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/7/67988%20Distinctive%20Features%20for%20Classification%20of.pdf http://irep.iium.edu.my/67988/13/67988%20Distinctive%20Features%20for%20Classification%20_scopus.pdf |
| _version_ | 1848786865322196992 |
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| author | Mohd Johari, Nabila Husna Abd Malik, Noreha Sidek, Khairul Azami |
| author_facet | Mohd Johari, Nabila Husna Abd Malik, Noreha Sidek, Khairul Azami |
| author_sort | Mohd Johari, Nabila Husna |
| building | IIUM Repository |
| collection | Online Access |
| description | Classification of respiratory sounds between
normal and abnormal is very crucial for screening and diagnosis
purposes. Lung associated diseases can be detected through this
technique. With the advancement of computerized auscultation
technology, the adventitious sounds such as crackles can be
detected and therefore diagnostic test can be performed earlier.
In this paper, Mel-frequency Cepstral Coefficient (MFCC) is
used to extract features from normal and crackles respiratory
sounds. By using statistical computation such as mean and
standard deviation (SD) of cepstral based coefficients it can
differentiate between crackles and normal sounds. The result
shows that the first three statistical values of SD of coefficients
provide distinctive feature between normal and crackles
respiratory sounds. Hence, MFCCs can be used as feature
extraction method of respiratory sounds to classify between
normal and crackles as screening and diagnostic tool. |
| first_indexed | 2025-11-14T17:15:49Z |
| format | Proceeding Paper |
| id | iium-67988 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:15:49Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-679882019-08-17T03:50:40Z http://irep.iium.edu.my/67988/ Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients Mohd Johari, Nabila Husna Abd Malik, Noreha Sidek, Khairul Azami TK7885 Computer engineering Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Mel-frequency Cepstral Coefficient (MFCC) is used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The result shows that the first three statistical values of SD of coefficients provide distinctive feature between normal and crackles respiratory sounds. Hence, MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool. IEEE 2018-11 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/67988/7/67988%20Distinctive%20Features%20for%20Classification%20of.pdf application/pdf en http://irep.iium.edu.my/67988/13/67988%20Distinctive%20Features%20for%20Classification%20_scopus.pdf Mohd Johari, Nabila Husna and Abd Malik, Noreha and Sidek, Khairul Azami (2018) Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients. In: 2018 7th International Conference on Computer and Communication Engineering (ICCCE), 19th-20th September 2018, Kuala Lumpur. https://ieeexplore.ieee.org/document/8539305 10.1109/ICCCE.2018.8539305 |
| spellingShingle | TK7885 Computer engineering Mohd Johari, Nabila Husna Abd Malik, Noreha Sidek, Khairul Azami Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients |
| title | Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients |
| title_full | Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients |
| title_fullStr | Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients |
| title_full_unstemmed | Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients |
| title_short | Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients |
| title_sort | distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients |
| topic | TK7885 Computer engineering |
| url | http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/7/67988%20Distinctive%20Features%20for%20Classification%20of.pdf http://irep.iium.edu.my/67988/13/67988%20Distinctive%20Features%20for%20Classification%20_scopus.pdf |