Abstract
Echoed parts of Quranic accent (Qiraat) signals are exposed to reverberation of signals especially if they are listened to in a conference room or the Quranic recordings found in different media such as the web. Quranic verse rules identification/Tajweed are prone to additive noise and may reduce classification results. This research work aims to present our work towards Quranic accents (Qiraat) identification, which emphasizes on acoustic echo cancellation (AEC) of all echoed Quranic signals during the preprocessing phase of the system development. In order to conduct the AEC, three adaptive algorithms known as affine projection (AP), least mean square (LMS), and recursive least squares (RLS) are used during the preprocessing phase. Once clean Quranic signals are produced, they undergo feature extraction and pattern classification phases. The Mel Frequency Cepstral Coefficients is the most widely used technique for feature extraction and is adopted in this research work, whereas probabilities principal component analysis (PPCA), K-nearest neighbor (KNN) and gaussian mixture model (GMM) are used for pattern classification. In order to verify our methodology, audio files have been collected for Surat Ad-Duhaa for five different Quranic accents (Qiraat), namely: (1) Ad-Duri, (2) Al-Kisaie, (3) Hafs an A’asem, (4) IbnWardan, and (5) Warsh. Based on our experimental results, the AP algorithm achieved 93.9 % accuracy rate against all pattern classification techniques including PPCA, KNN, and GMM. For LMS and RLS, the achieved accuracy rates are different for PPCA, KNN, and GMM, whereby LMS with PPCA and GMM achieved the same accuracy rate of 96.9 %; however, LMS with KNN achieved 84.8 %. In addition, RLS with PPCA and GMM achieved the same accuracy rate of 90.9 %; however, RLS with KNN achieved 78.8 %. Therefore, the AP adaptive algorithm is able to reduce the echo of Quranic accents (Qiraat) signals in a consistent manner against all pattern classification techniques.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abushariah, M. A. M. (2006). A vector quantization approach to isolated-word automatic speech recognition. Master Dissertation, University of Malaya, Malaysia.
Adapa N. S., & Bollu S. (2012). Performance analysis of different adaptive algorithms based on acoustic echo cancellation. Master Thesis, Blekinge Institute of Technology, 371 79 Karlskrona Sweden.
Affandi, A., Dobaie, A. M., & Husain, M. (2014). Digital Filters Design using Matlab with Graphical User Interface (GUI). Life Science Journal, 11(5), 336–348.
Al-Haddad, S. A. R., Samad, S. A., Hussain, A., Ishak, K. A., & Noor, A. O. A. (2009). Robust speech recognition using fusion techniques and adaptive filtering. American Journal of Applied Sciences, 6(2), 290–295.
AnamulHaque, Md, Kamrul Islam, A. K. M., & Imdadul Islam, Md. (2010). Demystifying the digital adaptive filters conducts in acoustic echo cancellation. Journal of Multimedia, 5(6), 568–579.
Ari, C., Aksoy, S., & Arıkan, O. (2012). Maximum Likelihood Estimation of Gaussian Mixture Models Using Stochastic Search. Journal Pattern Recognition, 45(7), 2804–2816.
Attarian, A., Danis, G., Gronsbell, J., Iervolino, G., & Tran, H. (2013). A Comparison of feature selection and classification algorithms in identifying baseball pitches. In Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS’2013), Hong Kong, Vol. I, pp 263–268.
Balen, J. V. (2011). Automatic Recognition of Samples in Musical Audio. Master Thesis. UniversitatPompeuFabra, Barcelona, Spain.
Chetouani, M., Gas, B., Zarader, J.L., & Chavy, C. (2002). Neural predictive coding for speech discriminant feature extraction: The DFE-NPC. In Proceedings of European Symposium on Artificial Neural Networks (ESANN’2002), Bruges, Belgium, pp. 275–280.
De Sena, E., Antonello, N., Moonen, M., & van Waterschoot, T. (2015). On the modeling of rectangular geometries in room acoustic simulations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(4), 774–786.
Deepika, M., & Sujatha, A. (2013). Noise Cancellation in Speech Signal Processing Using Adaptive Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 1(9), 743–746.
Diniz, P. S. R. (2008). Adaptive Filtering: Algorithms and Practical Implementations Springer. Boston, MA, 3rd Edn,. ISBN: 978-0-387-31274-3.
Gannamaneni, G.C. (2012). Acoustic echo cancellation inside a conference room using adaptive algorithms. Master Thesis, Blekinge Institute of Technology, Karlskrona, Sweden.
Goodwin, G. C., & Payne, R. L. (1977). Dynamic System Identification: Experiment Design and Data Analysis. New York: Academic Press.
Hadei, S. A. (2010). A family of adaptive filter algorithms in noise cancellation for speech enhancement. International Journal of Computer and Electrical Engineering, 2(2), 307–315.
Haykin S, (2002). Adaptive Filter Theory. 4thEdition, Prentice Hall.
Hosseinzadeh, D., & Krishnan, S. (2008). On the use of complementary spectral features for speaker recognition. EURASIP Journal on Advances in Signal Processing, 2008, 46.
Huang, X., Acero, A., & Hon, H. W. (2001). Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Upper Saddle River: Prentice Hall.
Hutson, M. (2003). Acoustic Echo Cancellation Using Digital Signal Processing. Project Report, The University of Queensland, Australia.
Islamway (2014). http://en.islamway.net/ Retrieved May 2014.
Ismail, M. N. I., & Muse, M. E. M. (2014). Probabilistic PCA mixture under variance preservation. World of Computer Science and Information Technology Journal (WCSIT), 4(8), 105–109.
Jacobsen, F., & Juhl, P. M. (2013). Fundamentals of General Linear Acoustics. United Kingdom: John Wiley & Sons Ltd.
Jarrett, D. P., Habets, E. A. P., Thomas, M. R. P., & Naylor, P. A. (2012). Rigid sphere room impulse response simulation: Algorithm and applications. The Journal of the Acoustical Society of America, 132(3), 1462–1472.
Kacur, J., Vargic, R., & Mulinka, P. (2011). Speaker identification by K-nearest neighbors: application of PCA and LDA prior to KNN. In IEEE Proceedings of the 18th International Conference on Systems, Signals and Image Processing (IWSSIP), Sarajevo, pp. 1–4.
Kamarudin, N., Al-Haddad, S. A. R., Hashim, S. J., Nematollahi, M. A., & Bin Hassan, A. R. (2014). Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification. In IEEE Proceedings of Student Conference on Research and Development (SCOReD’2014), Malaysia, pp. 1–6.
Khalifa, O., Khan, S., Islam, M. R., Faizal, M., & Dol, D. (2004).Text independent automatic speaker recognition. In 3rd International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, pp. 561–564.
Kourav, A., & Soni, B. K. (2011). RLS algorithm for adaptive echo cancellation. International Journal on Emerging Technologies, 2(2), 35–38.
Kuttruff, H. (2000). Room Acoustics (4th ed.). London, New York: Spon Press.
Lee, C-H., Chou, C-H., Lien, C-C., & Fang, J-C. (2011). Music genre classification using modulation spectral features and multiple prototype vectors representation. In IEEE Proceedings of the 4th International Congress on Image and Signal Processing (CISP’2011), Shanghai, China, Vol. 5, pp. 2762–2766.
Liu, K. R., Hsieh, S. F., & Yao, K. (1992). Systolic block householder transformation for RLS algorithm with two-level pipelined implementation. IEEE Transactions on Signal Processing, 40(4), 946–958.
Mahbub, U., Fattah, S. A., Zhu, W-P., & Ahmad, M.O. (2014).Single-channel acoustic echo cancellation in noise based on gradient-based adaptive filtering. EURASIP Journal on Audio, Speech, and Music Processing, 2014: 20.
Mousa, A., Qados, M., & Bader, S. (2011). Adaptive noise cancellation algorithms sensitivity to parameters. In IEEE Proceedings of the International Conference on Multimedia Computing and Systems (ICMCS’2011), Ouarzazate, Morocco, pp. 1–5.
Mousa, A., Qados, M., & Bader, S. (2012). Speech signal enhancement using adaptive noise cancellation techniques. Canadian Journal on Electrical and Electronics Engineering, 3(7), 375–383.
Munjal, A., Aggarwal, V., & Singh, G. (2008). Acoustic echo cancellation using RLS algorithm. In Proceedings of 2nd National Conference on Challenges and Opportunities in Information Technology (COIT-2008), MandiGobindgarh, India, pp 299–303.
Nguyen-Ky, T., Leis, J., & Xiang, W. (2010). An improved new error estimation algorithm for optimal filter lengths for stereophonic acoustic echo cancellation. Computers & Electrical Engineering, 36(4), 664–675.
Ozeki, K., & Umeda, T. (1984). An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties. Electronics and Communications in Japan (Part I: Communications), 67(5), 19–27.
Pallabi, P., & Bhavani, T. (2006). Face recognition using multiple classifiers. In IEEE Proceedings of the 18th International Conference on Tools with Artificial Intelligence (ICTAI’2006), Virginia, USA, pp. 179–186.
Parvin, S., & Park J. S. (2007). An efficient music retrieval using noise cancellation. In IEEE Proceedings of Future Generation Communication and Networking(FGCN’2007), South Korea, pp. 541–546.
Ramli, R. M., Noor, A. O. A., & Samad, S. A. (2012). A review of adaptive line enhancers for noise cancellation. Australian Journal of Basic and Applied Sciences, 6(6), 337–352.
Rao, K. S., & Koolagudi, S. G. (2013). Robust Emotion Recognition using Spectral and Prosodic Features., SpringerBriefs in Speech Technology New York: Springer.
Razak, Z., Ibrahim, N. J., Idris, M. Y. I., Tamil, E. M., MohdYusoff, M. Y. Z., & Abdul Rahman, N. N. (2008). Quranic verse recitation recognition module for support in j-QAF learning : A review. IJCSNS International Journal of Computer Science and Network Security, 8(8), 207–216.
Segura, J. C., Benitez M. C., de la Torre, A., Dupont, S., & Rubio, A. J. (2002). VTS Residual noise compensation. In IEEE Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP’2002), Florida, USA, pp. 409–412.
Stokes, J. W. & Malvar, H. S. (2004). Acoustic echo cancellation with arbitrary playback sampling rate. In IEEE Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP’04), Vol. 4, Canada, ppiv-153–iv-156.
Sudhir, V. V., Murthy, A. S. N., & Rani, D. E. (2014). Acoustic echo cancellation using adaptive algorithms. International Journal of Advances in Computer Science and Technology, 3(4), 248–252.
Tipping, M. E., & Bishop, C. M. (1999). Mixtures of probabilistic principal component analysers. Neural Computation, 11(2), 443–482.
Toda, T., Black, A. W., Tokuda, K. (2008). Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model. Journal Speech Communication, 50(3), 215–227
Tyagi, R., & Agrawal, D. (2012). Analysis the results of acoustic echo cancellation for speech processing using LMS adaptive filtering algorithm. International Journal of Computer Applications, 56(15), 7–11.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Kamarudin, N., Al-Haddad, S.A.R., Abushariah, M.A.M. et al. Acoustic echo cancellation using adaptive filtering algorithms for Quranic accents (Qiraat) identification. Int J Speech Technol 19, 393–405 (2016). https://doi.org/10.1007/s10772-015-9319-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-015-9319-z