Identifying the Qur’anic Segment from Video Recording

  • Haslizatul Mohamed HanumEmail author
  • Norizan Mat Diah
  • Zainab Abu Bakar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)


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).


Video segmentation Acoustic feature pattern Segment model 



This research is supported by Universiti Teknologi MARA (UiTM), Shah Alam, Selangor under the UiTM Internal Grant 600-IRMI/DANA 5/3/LESTARI (0111/2016). Special thanks to the members of the Faculty of Computer and Mathematical Sciences at UiTM for the encouragement to pursue research at the faculty. Thank you to Liliana Nulkasim for evaluating and manually segmenting the video contents and constructing the Malay speech collection closely supervised by the first author.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Haslizatul Mohamed Hanum
    • 1
    Email author
  • Norizan Mat Diah
    • 1
  • Zainab Abu Bakar
    • 2
  1. 1.Universiti Tenologi MARAShah AlamMalaysia
  2. 2.Al-Madinah International UniversityShah AlamMalaysia

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