Recognizing Music Features Pattern Using Modified Negative Selection Algorithm for Songs Genre Classification

  • Noor Azilah Muda
  • Azah Kamilah MudaEmail author
  • Choo Yun Huoy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)


Previous studies have proven that imitating the mechanism of recognizing alien cells is beneficial and provides so many solutions to the pattern recognition related problems. These efforts emulate the human immune system in recognizing the cells by considering every essential component or features of the subjects. In this research, the focus is on analyzing the music features patterns to recognize various songs genres by emphasizing the features from the artists’ voices, the melody of the music and even the sounds of the musical instruments used. Three fundamental music contents are investigated which are timbre, rhythm, and pitch based features. The main objective of this research is to recognize the music features from different genres using the modified negative selection algorithm fundamental procedures that are the censoring and monitoring modules. The results of the experimental works are remarkable and are comparable to previous works in the music recognition and classification works. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed algorithm and other classification technique are discussed.


Artificial immune system Modified AIS-based classifier Censoring and monitoring modules Classification Song genre 



This work is funded by Universiti Teknikal Malaysia Melaka (UTeM) through the PJP High Impact Research Grant [PJP/2016/FTMK/HI3/S01473].


  1. 1.
    de Casto, L.N., Timmis, J.: Artificial immune system: a new computational intelligence approach, pp. 76–79. Springer, Great Britain (2002)Google Scholar
  2. 2.
    Dasgupta, D.: Information processing mechanisms of the immune system. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization. McGraw Hill, London (1999)Google Scholar
  3. 3.
    Xiao, R.-B., Wang, L., Liu, Y.: A framework of AIS based pattern classification and matching for engineering creative design. In: IEEE First International on Machine Learning and Cybernetics, Beijing, China (2002)Google Scholar
  4. 4.
    Xiao, R.-B., Wang, L., Liu, Y.: A framework of AIS based pattern classification and matching for engineering creative design. In: Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, China, pp. 1554–1558 (2002)Google Scholar
  5. 5.
    Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, USA, pp. 202–212 (1994)Google Scholar
  6. 6.
    Muda, N.A., Wilson, C., Ling, S.: Modified AIS-based classifier for music genre classification. In: 11th International Society for Music Information Retrieval Conference, 9–13 August 2010, Utrecht, Netherlands (2010)Google Scholar
  7. 7.
    Muda, N.A., Ahmad, S., Muda, A.K.: Recognizing patterns of music signals songs classification using modified AIS-based classifier. In: Software Engineering and Computer Systems ICSECS 2011. Springer, Pahang, Malaysia, 27–29 June 2011Google Scholar
  8. 8.
    Golzari, S., Doraisamy, S., Sulaiman, M.N., Udzir, N.I.: A hybrid approach to traditional malay music genre classification: combining feature selection and artificial immune recognition system. In: Proceedings of International Symposium of Information Technology 2008, vol. 1–4, pp. 1068–1073 (2008a)Google Scholar
  9. 9.
    Golzari, S., Doraisamy, S., Sulaiman, M.N.B., Udzir, N.I. Norowi, N.M.: Artificial immune recognition system with nonlinear resource allocation method and application to traditional malay music genre classification. In: Proceedings of Artificial Immune Systems, vol. 5132, pp. 132–141 (2008b)Google Scholar
  10. 10.
    Costa, Y.M.G., Olivera, L.S., Silla, C.S.: An evaluation of convolutional neural networks for music classification using spectrograms. Appl. Soft Comput. J. 52, 28–38 (2017)CrossRefGoogle Scholar
  11. 11.
    Koukoutchos, M.: Music genre classification, The University of Washington (2017)Google Scholar
  12. 12.
    Rayar, R., Bennet, M.A., Banu, A.N., Sushanthi, A., Rajasekar, M.: Music instrument sound classification. IIOAB J. 8(2), 36–41 (2017)Google Scholar
  13. 13.
    Creme, M., Burlin, C., Lenain, R.: Music genre classification, Stanford University (2016)Google Scholar
  14. 14.
    Sillaa, C.N., KoerichH, A.L., Kaestner, C.A.A.: Improving automatic music genre classification with hybrid content-based feature vectors. In: 25th Symposium on Applied Computing, Sierre, Switzerland (2010)Google Scholar
  15. 15.
    Brecheisen, S., Kriegel, H.P., Kunath, P., Pryakhin, A.: Hierarchical genre classification for large music collections. In: 2006 Proceedings of IEEE International Conference on Multimedia and Expo - ICME 2006, vol. 1–5, pp. 1385–1388 (2006)Google Scholar
  16. 16.
    Li, T., Ogihara, M.: Toward intelligent music information retrieval. IEEE Trans. Multimedia 8, 564–574 (2006)CrossRefGoogle Scholar
  17. 17.
    Lippens, S., Martens, J.P., Mulder, T.D.: A comparison of human and automatic musical genre classification. In: Acoustics Speech and Signal Processing (2004)Google Scholar
  18. 18.
    Neumayer, R., Rauber, A.: Integration of text and audio features for genre classification in music information retrieval. In: Proceeding of 29th European Conference on Information Retrieval, Rome, Italy, pp. 724–727 (2007)Google Scholar
  19. 19.
    Sotiropaolos, D.N., Lampropaolos, A.S., Tsihrintzis, G.A.: Artificial immune system-based music genre classification. In: New Directions in Intelligent Interactive Multimedia, vol. 142, pp. 191–200 (2008)Google Scholar
  20. 20.
    Watkins, A.B.: AIRS: a resource limited artificial immune classifier. Computer Science, Mississippi State University, Mississippi (2001)Google Scholar
  21. 21.
    Hsu, J.-L., Liu, C.-C., Chen, A.L.P.: Discovering nontrivial repeating patterns in music data. IEEE Trans. Multimedia 3, 311–325 (2001)CrossRefGoogle Scholar
  22. 22.
    Liu, H., Setiono, R.: Feature selection via discretization. IEEE Trans. Knowl. Data Eng. 9, 642–645 (1997)CrossRefGoogle Scholar
  23. 23.
    Gonzalez, F., Dasgupta, D., Gomez, J.: The effect of binary matching rules in negative selection. In: Genetic and Evolutionary Computation — GECCO 2003. Springer, Heidelberg, Berlin (2003)CrossRefGoogle Scholar
  24. 24.
    Tzanetakis, G., Cook, P.: MARSYAS: a framework for audio analysis. Organized Sound 4, 169–175 (1999)CrossRefGoogle Scholar
  25. 25.
    Lidy, T.: Evaluations of new audio features and their utilization in novel music retrieval applications, Vienna University of Technology, Vienna (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Noor Azilah Muda
    • 1
  • Azah Kamilah Muda
    • 1
    Email author
  • Choo Yun Huoy
    • 1
  1. 1.Computational Intelligence and Technologies (CIT) Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication TechnologyUniversiti Teknikal Malaysia MelakaDurian TunggalMalaysia

Personalised recommendations