Distinctive features for normal and crackles respiratory sounds 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 detect...
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| Format: | Article |
| Language: | English English |
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Universitas Ahmad Dahlan
2019
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| Online Access: | http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/1/73673_Distinctive%20features%20for%20normal.pdf http://irep.iium.edu.my/73673/7/73673_Distinctive%20features%20for%20normal%20and%20crackles%20respiratory%20sounds%20using%20cepstral%20coefficients_Scopus.pdf |
| _version_ | 1848787837290283008 |
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| author | Mohd Johari, Nabila Husna Abdul Malik, Noreha Sidek, Khairul Azami |
| author_facet | Mohd Johari, Nabila Husna Abdul 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, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are 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 statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool. |
| first_indexed | 2025-11-14T17:31:16Z |
| format | Article |
| id | iium-73673 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:31:16Z |
| publishDate | 2019 |
| publisher | Universitas Ahmad Dahlan |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-736732019-11-24T15:35:42Z http://irep.iium.edu.my/73673/ Distinctive features for normal and crackles respiratory sounds using cepstral coefficients Mohd Johari, Nabila Husna Abdul 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, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are 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 statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool. Universitas Ahmad Dahlan 2019-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/73673/1/73673_Distinctive%20features%20for%20normal.pdf application/pdf en http://irep.iium.edu.my/73673/7/73673_Distinctive%20features%20for%20normal%20and%20crackles%20respiratory%20sounds%20using%20cepstral%20coefficients_Scopus.pdf Mohd Johari, Nabila Husna and Abdul Malik, Noreha and Sidek, Khairul Azami (2019) Distinctive features for normal and crackles respiratory sounds using cepstral coefficients. Bulletin of Electrical Engineering and Informatics, 8 (3). pp. 875-881. ISSN 2302-9285 http://journal.portalgaruda.org/index.php/EEI/article/view/1517 10.11591/eei.v8i3.1517 |
| spellingShingle | TK7885 Computer engineering Mohd Johari, Nabila Husna Abdul Malik, Noreha Sidek, Khairul Azami Distinctive features for normal and crackles respiratory sounds using cepstral coefficients |
| title | Distinctive features for normal and crackles respiratory sounds using cepstral coefficients |
| title_full | Distinctive features for normal and crackles respiratory sounds using cepstral coefficients |
| title_fullStr | Distinctive features for normal and crackles respiratory sounds using cepstral coefficients |
| title_full_unstemmed | Distinctive features for normal and crackles respiratory sounds using cepstral coefficients |
| title_short | Distinctive features for normal and crackles respiratory sounds using cepstral coefficients |
| title_sort | distinctive features for normal and crackles respiratory sounds using cepstral coefficients |
| topic | TK7885 Computer engineering |
| url | http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/1/73673_Distinctive%20features%20for%20normal.pdf http://irep.iium.edu.my/73673/7/73673_Distinctive%20features%20for%20normal%20and%20crackles%20respiratory%20sounds%20using%20cepstral%20coefficients_Scopus.pdf |