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

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.





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.