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Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim

Identifying Quran recitation segment from speech video recording has become one of an active research themes in speech processing and in application based on Quran education. Therefore, a more efficient method for video segment identification within long speech video recording that will consuming ti...

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Main Author: Nulkasim @ Mohd Kassim, Liliana
Format: Thesis
Language:English
Published: 2017
Subjects:
PN Literature (General)
Online Access:https://ir.uitm.edu.my/id/eprint/98101/1/98101.pdf
https://ir.uitm.edu.my/id/eprint/98101/
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spelling my.uitm.ir.981012024-08-21T23:27:49Z https://ir.uitm.edu.my/id/eprint/98101/ Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim Nulkasim @ Mohd Kassim, Liliana PN Literature (General) Identifying Quran recitation segment from speech video recording has become one of an active research themes in speech processing and in application based on Quran education. Therefore, a more efficient method for video segment identification within long speech video recording that will consuming time is urgently needed. This project develops a system to identify Quran recitation segment from speech video recording. This project applied manual video segmentation to differentiate between Quran and speech video content. This project selected 10 segmented video for Quran recitation and speech from one long speech video recording and extract the features using Praat tool. More specifically, two feature sets which are pitch and intensity are proposed to differentiate between Quran recitation and speech segment characteristics. A random forest classifier algorithm is employed in Spyder IDE using python language as a machine learning language for predict the type of an audio. The performance of the accuracy of the system will be trained and evaluated by the extracted audio features that will be compared with the segmented video which have been segmented manually. A classification accuracy of this project were 57% for pitch and 85% for intensity with the performance of 85% and 95% match accordingly. Therefore, by the accuracy of the result given has been proved that this project able to enhance the identification segment of Quran recitation. 2017 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/98101/1/98101.pdf Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim. (2017) Degree thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/98101.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic PN Literature (General)
spellingShingle PN Literature (General)
Nulkasim @ Mohd Kassim, Liliana
Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim
description Identifying Quran recitation segment from speech video recording has become one of an active research themes in speech processing and in application based on Quran education. Therefore, a more efficient method for video segment identification within long speech video recording that will consuming time is urgently needed. This project develops a system to identify Quran recitation segment from speech video recording. This project applied manual video segmentation to differentiate between Quran and speech video content. This project selected 10 segmented video for Quran recitation and speech from one long speech video recording and extract the features using Praat tool. More specifically, two feature sets which are pitch and intensity are proposed to differentiate between Quran recitation and speech segment characteristics. A random forest classifier algorithm is employed in Spyder IDE using python language as a machine learning language for predict the type of an audio. The performance of the accuracy of the system will be trained and evaluated by the extracted audio features that will be compared with the segmented video which have been segmented manually. A classification accuracy of this project were 57% for pitch and 85% for intensity with the performance of 85% and 95% match accordingly. Therefore, by the accuracy of the result given has been proved that this project able to enhance the identification segment of Quran recitation.
format Thesis
author Nulkasim @ Mohd Kassim, Liliana
author_facet Nulkasim @ Mohd Kassim, Liliana
author_sort Nulkasim @ Mohd Kassim, Liliana
title Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim
title_short Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim
title_full Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim
title_fullStr Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim
title_full_unstemmed Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim
title_sort identification of quran recitation segment from speech video recording / liliana nulkasim @ mohd kassim
publishDate 2017
url https://ir.uitm.edu.my/id/eprint/98101/1/98101.pdf
https://ir.uitm.edu.my/id/eprint/98101/
_version_ 1808976017855873024
score 13.211869

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