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
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id	my-uitm-ir.98101
record_format	uketd_dc
spelling	my-uitm-ir.981012024-08-21T23:27:49Z Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim 2017 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 https://ir.uitm.edu.my/id/eprint/98101/ https://ir.uitm.edu.my/id/eprint/98101/1/98101.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Mohamed Hanum, Haslizatul Fairuz
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohamed Hanum, Haslizatul Fairuz
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
qualification_level	Bachelor degree
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
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/98101/1/98101.pdf
_version_	1811768888629657600

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

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