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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)

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).

Keywords

Video segmentation Acoustic feature pattern Segment model 

Notes

Acknowledgment

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.

References

  1. 1.
    Prochazka, A., Kukal, J., Vysata, O.: Wavelet transform use for feature extraction and EEG signal segments classification. In: 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP), pp. 719–722. IEEE (2008)Google Scholar
  2. 2.
    Yang, H., Meinel, C.: Content based lecture video retrieval using speech and video text information. IEEE Trans. Learn. Technol. 7(2), 142–154 (2014)CrossRefGoogle Scholar
  3. 3.
    Abdo, M.S., Kandil, A.H.: Semi-automatic segmentation system for syllables extraction from continuous arabic audio. Signal 7(1), 535–540 (2016)Google Scholar
  4. 4.
    Alghamdi, M., El Hadj, Y.M., Alkanhal, M.: A manual system to segment and transcribe arabic speech. In: Signal Processing and Communications (ICSPC), pp. 233–236. IEEE (2007)Google Scholar
  5. 5.
    Hassan, H.A., Nasrudin, N.H., Khalid, M.N.M., Zabidi, A., Yassin, A.I.: Pattern classification in recognizing Qalqalah Kubra pronunciation using multilayer perceptrons. In: IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), pp. 209–212 IEEE (2012)Google Scholar
  6. 6.
    Hafeez, H., Mohiuddin, K., Ahmed, S.: Speaker-dependent live quranic verses recitation recognition system using Sphinx-4 framework. In: IEEE 17th International Multi-Topic Conference (INMIC), pp. 333–337 (2014)Google Scholar
  7. 7.
    Razak, Z., Ibrahim, N.J., Tamil, E.M., Idris, M.Y.I., Yusoff, Z.M.: Quranic verse recitation feature extraction using Mel-frequency cepstral co-efficient (MFCC). In: 4th International Colloquium on Signal Processing and Its Applications, pp. 978–983 (2008)Google Scholar
  8. 8.
    Hasan, M.R., Jamil, M., Rabbani, M.G., Rahman, M.S.: Speaker identification using mel frequency cepstral coefficients variations. In: International Conference on Electrical and Computer Engineering (ICECE), vol. 1, no. 4, Dhaka, Bangladesh (2004)Google Scholar
  9. 9.
    Chin-Hui, L., Soong, F.K., Biing-Hwang, J.: A segment model based approach to speech recognition. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 501–541 (1988)Google Scholar
  10. 10.
    Barrington, L., Chan, A.B., Gert, R.G.: Lanckriet: modeling music as a dynamic texture. IEEE Trans. Audio Speech Lang. Process. 18(3), 602–612 (2010)CrossRefGoogle Scholar
  11. 11.
    Thambi, S.V., Sreekumar, K.T., Kumar, C.S., Raj, P.R.: Random forest algorithm for improving the performance of speech/non-speech detection. In: 2014 First International Conference on Computational Systems and Communications (ICCSC), pp. 28–32. IEEE (2014)Google Scholar
  12. 12.
    Yang, L., Su, F.: Auditory context classification using random forests. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2349–2352. IEEE (2012)Google Scholar
  13. 13.
    Lu, L., Jiang, H., Zhang, H.: A robust audio classification and segmentation method. In Proceedings of the ninth ACM international conference on Multimedia, pp. 203–211. ACM (2001)Google Scholar
  14. 14.
    Hanum, H.M., Bakar, Z.A.: Sentence segmentation and phrase strength estimation in Malay continuous speech. In: Proceedings of the International Conference on Speech Prosody, vol. 2016, pp. 1163–1166 (2016)Google Scholar
  15. 15.
    Chan, A.B., Vasconcelos, N.: Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. Speech Audio Process. 30(5), 909–926 (2008)Google Scholar

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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