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Predicting Quranic Audio Clips Reciters Using Classical Machine Learning Algorithms: A Comparative Study

  • Ashraf Elnagar
  • Mohammed LataifehEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 874)

Abstract

This paper introduces a comparative analysis for a supervised classification system of Quranic audio clips of several reciters. Other than identifying the reciter or the closest reciter to an input audio clip, the study objective is to evaluate and compare different classifiers performing the stated recognition. With the widespread of multimedia capable devices with accessible media streams, several reciters became more popular than others for their distinct reciting style. It is quite common to find people who recite Quran in mimicry tone for popular reciters. Towards the achievement of a practical classifier system, a representative dataset of audio clips were constructed for seven popular reciters from Saudi Arabia. Key features were extracted from the audio clips, and different perceptual features such as pitch and tempo based features, short time energy were chosen. A combination of perceptual features were also completed in order to achieve better classification. The dataset was split into training and testing sets (\(80\%\) and \(20\%\), respectively). The classifier is implemented using several classifiers (SVM, SVM-Linear SVM-RBF, Logistic Regression, Decision Tree, Random Forest, Ensemble AdaBoost, and eXtreme Gradient Boosting. A cross comparative results for all acoustic features and top six subset are discussed for the selected classifiers, followed by fine-tuned parameters from classifiers defaults to optimize results. Finally we conclude with the results that suggest high accuracy performance for the selected classifiers averaging above \(90\%\) and an outstanding performance for XGBoosting reaching an accuracy rate above \(93\%\).

Keywords

Audio clips Arabic language The Quran Speaker recognition Classification Machine learning 

Notes

Acknowledgements

We would like to thank Rotana Ismail, Bahja Alattas, and Alia Alfalasi for initiating the work and constructing the dataset. We extend our thanks to the University of Sharjah for funding this work under targeted research project no.: 1702141151-P.

References

  1. 1.
    J.H.L. Hansen, T. Hasan, Speaker recognition by machines and humans: a tutorial review. IEEE Signal Process. Mag. 32(6), 74–99 (2015)CrossRefGoogle Scholar
  2. 2.
    M.A. Sadek, I. Md Shariful, H. Md Alamgir, Gender recognition system using speech signal. Int. J. Comput. Sci. Eng. Inf. Technol. 2(1), 1–9 (2012)Google Scholar
  3. 3.
    F. Alías, J.C. Socoró, X. Sevillano, A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds. Appl. Sci. 6(5) (2016)CrossRefGoogle Scholar
  4. 4.
    J.H. Bach, J. Anemüller, B. Kollmeier, Robust speech detection in real acoustic backgrounds with perceptually motivated features. Speech Commun. 53(5), 690–706 (2011)CrossRefGoogle Scholar
  5. 5.
    Y. Yaslan, Z. Cataltepe, Music genre classification using audio features, different classifiers and feature selection methods, in 2006 IEEE 14th Signal Processing and Communications Applications, vols. 1 and 2 (2006), pp. 535–538Google Scholar
  6. 6.
    A. Ghosal, S. Dutta, Automatic male-female voice discrimination, in Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2014, February 2014, pp. 731–735Google Scholar
  7. 7.
    H. Harb, L. Chen, Gender identification using a general audio classifier, in Proceedings of the IEEE International Conference on Multimedia and Expo, vol. 2 (2003), pp. II733–II736Google Scholar
  8. 8.
    P. Mini, T. Thomas, R. Gopikakumari, Feature vector selection of fusion of MFCC and SMRT coefficients for SVM classifier based speech recognition system, in Proceedings of the 8th International Symposium on Embedded Computing and System Design, ISED 2018, pp. 152–157Google Scholar
  9. 9.
    M. Maqsood, A. Habib, T. Nawaz, An efficient mispronunciation detection system using discriminative acoustic phonetic features for Arabic consonants. Int. Arab J. Inf. Technol. 16(2), 242–250 (2019)Google Scholar
  10. 10.
    H. Meinedo, I. Trancoso, Age and gender classification using fusion of acoustic and prosodic features, in Interspeech-2010, January 2010, pp. 2818–2821Google Scholar
  11. 11.
    H. Kim, N. Moreau, T. Sikora, Audio classification based on MPEG-7 spectral basis representations. IEEE Trans. Circuits Syst. Video Technol. 14(5), 716–725 (2004)CrossRefGoogle Scholar
  12. 12.
    C. Okuyucu, M. Sert, A. Yazici, Audio feature and classifier analysis for efficient recognition of environmental sounds, in Proceedings of the 2013 IEEE International Symposium on Multimedia, ISM 2013 (2013), pp. 125–132Google Scholar
  13. 13.
    J.-C. Wang, J.-F. Wang, K.W. He, C.-S. Hsu, Environmental sound classification using hybrid SVM/KNN classifier and MPEG-7 audio low-level descriptor, in International Joint Conference on Neural Networks, 2006, pp. 1731–1735Google Scholar
  14. 14.
    L.T. Christopher, J.C. Burges, A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)CrossRefGoogle Scholar
  15. 15.
    B. Moghaddam, M.H. Yang, Gender classification with support vector machines, in Proceedings of the 4th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2000, 2000, pp. 306–311Google Scholar
  16. 16.
    A. Elnagar, Y.S. Khalifa, A. Einea, Hotel Arabic—reviews dataset construction for sentiment analysis applications, in Studies in Computational Intelligence, vol. 740, ed. by K. Shaalan, A. Hassanien, F. Tolba (Springer, 2017), pp. 35–52Google Scholar
  17. 17.
    A. Elnagar, A. Einea, Investigation on sentiment analysis of Arabic book reviews, in IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016, pp. 1–7Google Scholar
  18. 18.
    A. Elnagar, O. Einea, Book reviews in Arabic dataset, in IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016, pp. 1–8Google Scholar
  19. 19.
    A. Pahwa, G. Aggarwal, Speech feature extraction for gender recognition. Int. J. Image Graph. Signal Process. 8(9), 17–25 (2016)CrossRefGoogle Scholar
  20. 20.
    S.S. Al-Dahri, Y.H. Al-Jassar, Y.A. Alotaibi, M.M. Alsulaiman, K. Abdullah-Al-Mamun, A word-dependent automatic Arabic speaker identification system, in IEEE International Symposium on Signal Processing and Information Technology, 2008, pp. 198–202Google Scholar
  21. 21.
    A. Krobba, M. Debyeche, A. Amrouche, Evaluation of speaker identification system using GSMEFR speech data, in International Conference on Design & Technology of Integrated Systems in Nanoscale Era, 2010, pp. 1–5Google Scholar
  22. 22.
    A. Mahmood, M. Alsulaiman, G. Muhammad, Automatic speaker recognition using multi-directional local features (MDLF). Arab. J. Sci. Eng. 39(5), 3799–3811 (2014)CrossRefGoogle Scholar
  23. 23.
    H. Tolba, A high-performance text-independent speaker identification of Arabic speakers using a CHMM-based approach. Alexandria Eng. J. 50(1), 43–47 (2011)CrossRefGoogle Scholar
  24. 24.
    K. Saeed, M.K. Nammous, A speech-and-speaker identification system: feature extraction, description, and classification of speech-signal image. IEEE Trans. Ind. Electron. 54(2), 887–897 (2007)CrossRefGoogle Scholar
  25. 25.
    I. Shahin, A.B. Nassif, M. Bahutair, Emirati-accented speaker identification in each of neutral and shouted talking environments. Int. J. Speech Technol. 21(2), 265–278 (2018)CrossRefGoogle Scholar
  26. 26.
    F.M. Denny, Qur’an recitation: a tradition of oral performance and transmission. Oral Tradit. 4(1–2), 5–26 (1989)Google Scholar
  27. 27.
    S.A.E. Mohamed, A.S. Hassanin, M. Taher, B. Othman, Virtual Learning System (Miqra’ah) for Quran Recitations for Sighted and Blind Students (2014), pp. 195–205CrossRefGoogle Scholar
  28. 28.
    B. Yousfi, Holy Qur’an speech recognition system Imaalah checking rule for Warsh recitation, in Proceedings of the 13th International Colloquium on Signal Processing and its Applications, CSPA 2017, pp. 258–263.  https://doi.org/10.1109/CSPA.2017.8064962
  29. 29.
    K. Nelson, The Art of Reciting the Qur’an (University of Texas Press, 1985)Google Scholar
  30. 30.
    A. Abdurrochman, R.D. Wulandari, N. Fatimah, The comparison of classical music, relaxation music and the Qur’anic recital: an AEP study, in The 2007 Regional Symposium on Biophysics and Medical Physics, November 2007Google Scholar
  31. 31.
    A. Ghiasi, The effect of listening to holy quran recitation on anxiety: a systematic review. Iran. J. Nurs. Midwifery Res. 23(6), 411–420 (2018)CrossRefGoogle Scholar
  32. 32.
    J.I. Noor, M. Razak, N. Rahman, Automated tajweed checking rules engine for Quranic learning. Multicult. Educ. Technol. J. 7(4), 275–287 (2013)CrossRefGoogle Scholar
  33. 33.
    F. Thirafi, Hybrid HMM-BLSTM-based acoustic modeling for automatic speech recognition on Quran recitation, in Proceedings of the International Conference on Asian Language Processing, IALP 2018, pp. 203–208.  https://doi.org/10.1109/IALP.2018.8629184
  34. 34.
    J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999).  https://doi.org/10.1023/A:1018628609742CrossRefGoogle Scholar
  35. 35.
    L. Sun, Decision tree SVM model with Fisher feature selection for speech emotion recognition. Eurasip J. Audio Speech Music Process. 1(1) (2019)Google Scholar
  36. 36.
    D.W. Hosmer Jr., S. Lemeshow, R.X. Sturdivant, Applied Logistic Regression, 3rd edn. (Wiley, 2013).  https://doi.org/10.1002/9781118548387CrossRefGoogle Scholar
  37. 37.
    J.R. Quinlan, Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986).  https://doi.org/10.1007/BF00116251Google Scholar
  38. 38.
    A. Liaw, M. Wiener, Classification and regression by Random Forest. R News 2, 18–22 (2002)Google Scholar
  39. 39.
    Y. Freund, R.E. Schapire, A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771–780 (1999)Google Scholar
  40. 40.
    J.H. Friedman, Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  41. 41.
    T. Al Bakri, M. Mallah, Musical performance of the holy Quran with assistance of the Arabic Maqams. Turk. Online J. Educ. Technol. 2016, 121–132 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of SharjahSharjahUnited Arab Emirates

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