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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Identifying the Qur’anic Segment from Video Recording
verfasst von : Haslizatul Mohamed Hanum, Norizan Mat Diah, Zainab Abu Bakar
Erschienen in: Advances in Visual Informatics
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Abstract
This paper describes a system to identify Quran recitation (referred as Qur’anic) segment from speech video recording using the extracted acoustic signal. Identifying the Qur’anic sequence pattern from mixed-combination of speech and Qur’anic signal will contribute to more efficient segmentation of video segments. The random forest classifier algorithm is employed to classify the dynamic pattern of the extracted audio. Two feature sets which are pitch and intensity are extracted from the audio, and constructed into sequence of speech patterns which then classified as Qur’anic or non-Quranic segments. A collection of 40 segmented videos were trained and compared with the segmented videos which have been segmented manually. This project achieves classification accuracy of 57% using pitch and 85% using intensity. While using pitch feature only, 85% of the identified segments match the manually segmented collection while using intensity feature gives 95% match accordingly).