Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification
This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. Theref...
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2014
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/68279/ http://psasir.upm.edu.my/id/eprint/68279/1/Feature%20extraction%20using%20spectral%20centroid%20and%20mel%20frequency%20cepstral%20coefficient%20for%20Quranic%20accent%20automatic%20identification.pdf |
| _version_ | 1848856082004312064 |
|---|---|
| author | Kamarudin, Noraziahtulhidayu Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Nematollahi, Mohammad Ali Hassan, Abd Rauf |
| author_facet | Kamarudin, Noraziahtulhidayu Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Nematollahi, Mohammad Ali Hassan, Abd Rauf |
| author_sort | Kamarudin, Noraziahtulhidayu |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. Therefore, to improve the performance of MFCC with addition of Spectral Centroid features and is proposed for used in feature extraction of Quranic accents. Through implementing the Spectral Centroid Feature, it complements in improving the accuracy result of identifying the Quranic accents. The pattern classification algorithm here used the dimensional reduced technique from Probabilistic Principal Component Analysis (PPCA) on the features and Gaussian Mixture Model, in purpose to model the effectiveness of both combination of feature extraction. The accuracy of automatic identification for such Quranic Accents are found increasing from 96.9% to 100% with the application of SCF. |
| first_indexed | 2025-11-15T11:36:00Z |
| format | Conference or Workshop Item |
| id | upm-68279 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:36:00Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-682792019-05-10T08:29:30Z http://psasir.upm.edu.my/id/eprint/68279/ Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification Kamarudin, Noraziahtulhidayu Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Nematollahi, Mohammad Ali Hassan, Abd Rauf This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. Therefore, to improve the performance of MFCC with addition of Spectral Centroid features and is proposed for used in feature extraction of Quranic accents. Through implementing the Spectral Centroid Feature, it complements in improving the accuracy result of identifying the Quranic accents. The pattern classification algorithm here used the dimensional reduced technique from Probabilistic Principal Component Analysis (PPCA) on the features and Gaussian Mixture Model, in purpose to model the effectiveness of both combination of feature extraction. The accuracy of automatic identification for such Quranic Accents are found increasing from 96.9% to 100% with the application of SCF. IEEE 2014 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68279/1/Feature%20extraction%20using%20spectral%20centroid%20and%20mel%20frequency%20cepstral%20coefficient%20for%20Quranic%20accent%20automatic%20identification.pdf Kamarudin, Noraziahtulhidayu and Syed Mohamed, Syed Abdul Rahman Al-Haddad and Hashim, Shaiful Jahari and Nematollahi, Mohammad Ali and Hassan, Abd Rauf (2014) Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification. In: 2014 IEEE Student Conference on Research and Development (SCOReD), 16-17 Dec. 2014, Penang, Malaysia. . 10.1109/SCORED.2014.7072945 |
| spellingShingle | Kamarudin, Noraziahtulhidayu Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Nematollahi, Mohammad Ali Hassan, Abd Rauf Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification |
| title | Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification |
| title_full | Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification |
| title_fullStr | Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification |
| title_full_unstemmed | Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification |
| title_short | Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification |
| title_sort | feature extraction using spectral centroid and mel frequency cepstral coefficient for quranic accent automatic identification |
| url | http://psasir.upm.edu.my/id/eprint/68279/ http://psasir.upm.edu.my/id/eprint/68279/ http://psasir.upm.edu.my/id/eprint/68279/1/Feature%20extraction%20using%20spectral%20centroid%20and%20mel%20frequency%20cepstral%20coefficient%20for%20Quranic%20accent%20automatic%20identification.pdf |