Advertisement

Sādhanā

, 44:40 | Cite as

Mean centred clustering: improving melody classification using time- and frequency-domain supervised clustering

  • CHANDANPREET KAUREmail author
  • RAVI KUMAR
Article
  • 10 Downloads

Abstract

This paper reports a new approach for clustering melodies in audio music collections of both western as well as Indian background and its application to genre classification. A simple yet effective new classification technique called mean centred clustering (MCC) is discussed. The proposed technique maximizes the distance between different clusters and reduces the spread of data in individual clusters. The use of MCC as a preprocessing technique for conventional classifiers like artificial neural network (ANN) and support vector machine (SVM) is also demonstrated. It is observed that the MCC-based classifier outperforms the classifiers based on conventional techniques such as Principal Component Analysis (PCA) and discrete cosine transform (DCT). Extensive simulation results obtained on different data sets of western genre (ISMIR) and classical Indian ragas are used to validate the efficiency of proposed MCC-based clustering algorithm and ANN/SVM classifiers based on MCC. As an additional endeavour, the performance of MCC on preprocessed data from PCA and DCT is studied. Based on simulation results, it is concluded that the application of MCC on DCT coefficients resulted in the highest overall classification success rate over different architectures of the classifiers.

Keywords

Artificial neural network mean centre clustering musical genre classification pattern clustering method support vector machine 

References

  1. 1.
    Muller M, Ellis D P, Klapuri A and Richard G 2011 Signal processing for music analysis. IEEE Journal of Selected Topics in Signal Processing 5(6): 1088–1110CrossRefGoogle Scholar
  2. 2.
    Htike K K 2018 Forests of unstable hierarchical clusters for pattern classification. Soft Computing 22(5): 1711–1718CrossRefGoogle Scholar
  3. 3.
    Frakes W B and Baeza Yates R (Eds) 1992 Information retrieval: data structures & algorithms. Englewood Cliffs, New Jersey: Prentice-HallGoogle Scholar
  4. 4.
    Sangam R S and Om H 2018 An equi-biased k-prototypes algorithm for clustering mixed-type data. Sadhana 43(3): 37MathSciNetCrossRefGoogle Scholar
  5. 5.
    Seltzer M L, Raj B and Stern R M 2004 A Bayesian classifier for spectrographic mask estimation for missing feature speech recognition. Speech Communication 43(4): 379–393CrossRefGoogle Scholar
  6. 6.
    Rabiner L R 1989 A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2): 257–286CrossRefGoogle Scholar
  7. 7.
    Purohit P and Joshi R 2013 A new efficient approach towards K-means clustering algorithm. International Journal of Computer Applications 65(11): 7–10Google Scholar
  8. 8.
    Jose A, Ravi S and Sambath M 2014 Brain tumor segmentation using k-means clustering and fuzzy c-means algorithms and its area calculation. International Journal of Innovative Research in Computer and Communication Engineering 2(3): 3496–3501Google Scholar
  9. 9.
    Yedla M, Pathakota S R and Srinivasa T M 2010 Enhancing K-means clustering algorithm with improved initial center. International Journal of Computer Science and Information Technologies 1(2): 121–125Google Scholar
  10. 10.
    Daisy V R and Nirmala S 2018 Stability-integrated fuzzy C means segmentation for spatial incorporated automation of number of clusters. Sadhana 43(3): 40MathSciNetCrossRefGoogle Scholar
  11. 11.
    Sharma A K, Lakhtaria K I, Panwar A and Vishwakarma S 2014 An analytical approach based on self organized maps (SOM) in Indian classical music raga clustering. In: Proceedings of the Seventh International Conference on Contemporary Computing (IC3), IEEE, pp. 449–453Google Scholar
  12. 12.
    Kirthika P and Chattamvelli R 2012 A review of raga based music classification and music information retrieval (MIR). In: Proceedings of the IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA), pp. 1–5Google Scholar
  13. 13.
    Levy M and Sandler M 2008 Structural segmentation of musical audio by constrained clustering. IEEE Transactions on Audio, Speech, and Language Processing 16(2): 318–326CrossRefGoogle Scholar
  14. 14.
    Foote J 2000 Automatic audio segmentation using a measure of audio novelty. In: Proceedings of the IEEE International Conference on Multimedia and Expo, vol. 1, pp. 452–455CrossRefGoogle Scholar
  15. 15.
    Maddage N C, Xu C, Kankanhalli M S and Shao X 2004 Content-based music structure analysis with applications to music semantics understanding. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, pp. 112–119Google Scholar
  16. 16.
    Kunz D 2008 An orientation-selective orthogonal lapped transform. IEEE Transactions on Image Processing 17(8): 1313–1322MathSciNetCrossRefGoogle Scholar
  17. 17.
    Schubert E, Canazza S, De Poli G and Roda A 2017 Algorithms can mimic human piano performance: the deep blues of music. Journal of New Music Research 46(2): 175–186CrossRefGoogle Scholar
  18. 18.
    Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J and Aulagnier S 1996 Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90(1): 39–52CrossRefGoogle Scholar
  19. 19.
    Vapnik V 1998 Statistical learning theory. New York: WileyzbMATHGoogle Scholar
  20. 20.
    Harris T 2015 Credit scoring using the clustered support vector machine. Expert Systems with Applications 42(2): 741–750CrossRefGoogle Scholar
  21. 21.
    Nawi N M, Atomi W H and Rehman M Z 2013 The effect of data pre-processing on optimized training of artificial neural networks. Procedia Technology 11: 32–39CrossRefGoogle Scholar
  22. 22.
    Kapoor S S 2005 Guru Granth Sahib—an advanced study, vol. I. New Delhi: Hemkunt PressGoogle Scholar
  23. 23.
    Benward B 2014 Music in theory and practice, vol. 1. New York: McGraw-Hill Higher EducationGoogle Scholar
  24. 24.
    Sudha R, Kathirvel A and Sundaram R D 2009 A system of tool for identifying ragas using MIDI. In: Proceedings of the Second International Conference on Computer and Electrical Engineering, ICCEE’09, vol. 2, pp. 644–647Google Scholar
  25. 25.
    Homburg H, Mierswa I, Moller B, Morik K and Wurst M 2005 A benchmark dataset for audio classification and clustering. In: Proceedings of ISMIR 2005, pp. 528–531Google Scholar
  26. 26.
    Davies D L and Bouldin D W 1979 A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2): 224–227CrossRefGoogle Scholar
  27. 27.
    Daniel H and Revathi A 2015 Raga identification of carnatic music using iterative clustering approach. In: Proceedings of the International Conference on Computing and Communications Technologies (ICCCT), IEEE, pp. 19–24Google Scholar
  28. 28.
    Haykin S S 2009 Neural networks and learning machines, 3rd ed. Upper Saddle River, NJ, USA: PearsonGoogle Scholar
  29. 29.
    Suthaharan S 2016 Machine learning models and algorithms for big data classification. Boston: SpringerCrossRefGoogle Scholar
  30. 30.
    Iosifidis A and Gabbouj M 2016 Multi-class support vector machine classifiers using intrinsic and penalty graphs. Pattern Recognition 55: 231–246CrossRefGoogle Scholar
  31. 31.
    Bordes A, Ertekin S, Weston J and Bottou L 2005 Fast kernel classifiers with online and active learning. Journal of Machine Learning Research 6: 1579–1619MathSciNetzbMATHGoogle Scholar
  32. 32.
    Zanaty E A 2012 Support vector machines (SVMs) versus multilayer perception (MLP) in data classification. Egyptian Informatics Journal 13(3): 177–183CrossRefGoogle Scholar
  33. 33.
    Bergmeir C and Benitez J M 2012 On the use of cross-validation for time series predictor evaluation. Information Sciences 191: 192–213CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Electronics and Computer EngineeringThapar UniversityPatialaIndia

Personalised recommendations