Bio-inspired Audio Content-Based Retrieval Framework (B-ACRF)

Authors: Noor A. Draman, Campbell Wilson, Sea Ling

Abstract:

Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.

Keywords: Bio-inspired audio content-based retrieval framework, features selection technique, low/high level features, artificial immune system

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331367

References:

[1] R. Neumayer, "Musical genre classification using a multi layer perceptron", In Proceedings of the 5th Workshop on Data Analysis (WDA'04), Tatranska Polianka, Slovak Republic, 2004, pp 51-66.
[2] O. Kotov, A. Paradzinets and E. Bovbels, "Musical genre classification using modified wavelet-like features and support vector machines", In Proceedings of the IASTED European Conference on Internet and Multimedia Systems and Application, Cambridge, United Kingdom, 2007.
[3] U.Bagsci, "Automatic classification of musical genre using inter-genre similarity", Journals of IEEE Signals Processing Letters, August 2007vol. 14, no. 8, pp. 521-524.
[4] T. Lambrou, P. Kudumakis, R. Speller, M.Sandler and A Linney, "Classification of audio signals using statistical features on time and wavelet transform domain", In Proc. Int. Conference on Acoustic, Speech and Signal Processing (ICASSP - 98), 1998, vol. 6, pp 3621- 3624.
[5] H. Soltau, T. Schultz and M. Westphal, "Recognition of music types", In Proceedings of the 1998 IEEE International Conference on Acoustic, Speech and Signal Processing, 1998, Denver, pp 1137-1140.
[6] A. Rauber and M. Fruhwirth, "Automatically analyzing and organizing music archives", In Proceedings of the European Conference on Research and Advanced Technology for Digital Libraries (ECDL), Darmstadt, Germany September 2001, pp. 402-414.
[7] T. Lidy and A. Rauber, "Evaluation of feature extractors and psychoacoustic transformations for music genre classification", In Proceeding of the 6th International Conference on Music Information Retrieval (ISMIR -05), 2005, pp. 34-41.
[8] T. Li, M. Ogihara and Q. Li, "A comparative study on content-based music genre classification", Proceedings of the 26th annual international ACM SIGIR, 2003, Toronto, Canada, pp. 282-289.
[9] G. Tzanetakis and P. Cook, "Musical genre classification of audio signals", IEEE Transactions on Speech and Audio Processing, vol.10, no.5, 2002, pp. 293-302.
[10] T. Li and M.Ogihara, "Toward intelligent music information retrieval", In Proceedings of IEEE Transactions on Multimedia, June 2006, vol. 8, No 3, pp. 564-574.
[11] M.McKinney and J. Breebaart, "Features for audio and music classification", In Proceeding ISMIR, 2003, pp. 151-158.
[12] C.R. Lin, N.H. Liu, Y.H. Wu and A.L.P. Chen, "Music classification using significant repeating patterns", In Proceedings Database Systems for Advanced Applications, 2004, pp. 506-518.
[13] I. Karydis, A. Nanopoulos and Y. Manolopoulos, "Symbolic musical genre classification based on repeating patterns", in Proceedings of the ACM Multimedia Workshop on Audio and Music for Multimedia (AMCMM), Santa Barbara, California, USA, 2006, pp. 53-58.
[14] F. Moerchen, I. Mierswa and A. Ultsch, "Understandable models of music collections based on exhaustive feature generation with temporal statistics", In Proceedings of International Conference on Knowledge Discovery and Data, Philadelphia, USA, 2006, pp. 882-891.
[15] R. Neumayer and A Rauber, "Integration of text and audio features for genre classification in music information retrieval", In Proceeding of 29th European Conference on Information Retrieval, Rome, Italy, 2007, pp. 724-727.
[16] F.M. El-Hadidy, H. J. G. de Poot and D. D. Velthausz, "Multimedia information retrieval framework: From theory to practice," in Proc. 8th Working Conference on Database Semantics- Semantic Issues in Multimedia Systems, Deventer, Netherlands, 1999, pp. 271-299.
[17] M. Gabbouj, S. Kiranyaz, K. Caglar, B. Cramariuc, F. Alaya Cheikh, 0. Guldogan, and E. Karaoglq, "MUVIS: A multimedia browsing, indexing and retrieval system," in Proc of the WDC 2002 Conference on Advanced Methodr for Multimedia Signal Processing, Capri, Italy 2003, pp. 1-8.
[18] K.-S. Park, W.-J Yoon, K.-K. Lee, S.-H. Oh and K.-M. Kim, "MRTB framework: a robust content-based music retrieval and browsing," in IEEE Transactions on Consumer Electronics, 2005, vol. 51, issue 1, pp. 117-122.
[19] M. Bosma, R. C. Veltkamp and F. Wiering, "MUGGLE: A music retrieval experimentation framework", in Proceedings of 9th International Conference on Music Perception and Cognition, Italy, 2006, pp. 1297-1303.
[20] X. Amatriain, M. de Boer, E. Robledo and D. Garcia , "CLAM: an OO framework for developing audio and music applications", In Companion of the 17th annual ACM SIGPLAN Conference on Objectoriented programming, systems, languages and applications (OOPSLA -02), Washington, USA, 2002, pp. 22-23.
[21] G. Tzanetakis and p. Cook, "Marsyas: A framework for audio analysis", in Organised Sound Journal, vol. 4 issue 3, 1999, pp. 169-175.
[22] K. Kosina, "Music Genre Recognition," M.S. thesis, Technical College Hagenberg, Austria, 2002.
[23] J. Liang, S. Yang and A. Winstanley, "Invariant optimal feature selection: A distance discriminant and features ranking based solution", Pattern Recognition Society, 2007, pp. 1429-1439.
[24] R. Kumar, V.K. Jayaraman and B. D. Kulkarni, "An SVM classifier incorporating simultaneous noise reduction and feature selection: Ilustrative case examples", Pattern Recognition, vol. 38, issue 1, 2005, pp. 41-49.
[25] L.N. de Casto and J. Timmis, "Artificial immune system: A new computational intelligence approach", Great Britain, Springer, 2002, pp. 76-79.
[26] S. Doraisamy, S. Golzari, N. M. Norowi, M. N. Sulaiman and N. I. Udzir, "A study on feature selection and classification techniques for automatic genre classification of traditional Malay music", in Proceedings of Ninth International Conference on Music Information Retrieval (ISMIR-08), Philadelphia, Pennsylvania USA, 2008, pp. 331- 336.
[27] D.N. Sotiropoulus, A.S. Lampropoulus and G.A. Tsihrintzis, "Artificial immune system-based music genre classification", in New Directions in Intelligent Interactive Multimedia, 2008, pp. 191-200.
[28] M. Caetamo, J. Manzolli and F. J. Von Zuben, " Application of an artificial immune system in a compositional timbre design technique", in Proceedings of International Conference on Artificial Immune Systems, Baff, Alberta, Canada, 2005, pp. 389-403.
[29] R.-B. Xiao, L. Wang and Y. Liu, "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, 2002, pp. 1554-1558.
[30] S. Forrest, A.S. Perelson, L. Allen and R. Cherukuri, "Self-nonself discrimination in a computer", in Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, USA,1994, pp. 202-212.
[31] K.-K. Lee and K.-S. Park, "Robust feature extraction for automatic classification of Korean traditional music in digital library", in Proceedings of 8th International Asian Digital Library, Bangkok, Thailand, 2005, pp.167-170.