A Survey of Style Identification Approaches in Music Information Retrieval
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2016 by IJCTT Journal | ||
| Volume-36 Number-1 |
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| Year of Publication : 2016 | ||
| Authors : Santosh Pakhare, Mrs M. A. Potey | ||
| DOI : 10.14445/22312803/IJCTT-V36P104 |
Santosh Pakhare, Mrs M. A. Potey "A Survey of Style Identification Approaches in Music Information Retrieval". International Journal of Computer Trends and Technology (IJCTT) V36(1):17-21, June 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
One of the problems to solve in Music
Information Retrieval (MIR) is the modelization of
music style. The system could be trained to identify the
main features that would characterize music genres or
style so as to look for that kind of music over large
musical corpus. So in this paper multimodal approach,
pattern recognition approach and co-updating
approach is been studied for identifying the style from
different genre of the music. Considering the intuitive
feelings of similarity from the listeners perspective,
the focus on features that are computed using
similarity metrics for melodies, harmonies, and audio
signals for style identification. A multimodal approach
mostly considered support vector machine as a binary
classifier to determine if two songs or music played by
the same artist given their similarity metrics in the
three aspects and also discussed the experimental
methodologies of the two different approaches.
References
[1] Shi-Huang Chen and Shih-Hao Chen. “Content-based music
genre classification using timbral feature vectors and
support vector machine.” In Proceedings of the 2Nd
International Conference on Interaction Sciences:
Information Technology, Culture and Human, ICIS `09,
pages 1095{1101, New York, NY, USA, 2009.
[2] Ching-Hua Chuan. “Pop-rock musical style as defined by
two-chord patterns at segmentation points in the melody and
lyrics.” In Multimedia, 2009. ISM `09. 11th IEEE
International Symposium on, pages 448{452, Dec 2009.
[3] Ching-Hua Chuan. “Harmonic style-based song retrieval
using n-gram.” In Proceedings of the 1st ACM International
Conference on Multimedia Retrieval, ICMR `11, pages
17:1{17:7, New York, NY, USA, 2011. ACM.
[4] Ching-Hua Chuan. “Audio classification and retrieval using
wavelets and Gaussian mixture models”. volume 4, pages
1{20, Hershey, PA, USA, January 2013.
[5] Ching-Hua Chuan. “A multimodal approach to song-level
style identification in pop/rock using similarity metrics”. In
Proceedings of the 2013 12th International Conference on
Machine Learning and Applications - Volume 02, ICMLA
`13, pages 321{324, Washington, DC, USA, 2013. IEEE
Computer Society.
[6] Maxime Crochemore, Christos Makris, Wojciech Rytter,
Athanasios Tsakalidis, and Kostas Tsichlas. Approximate
“string matching with gaps”. 2002.
[7] Roger B. Dannenberg, Belinda Thom, and David Watson. “A
machine learning approach to musical style recognition.” In
Proc. International Computer Music Conference, pages
344{347, 1997.
[8] P.P. de Leon and J. Inesta. “Pattern recognition approach for
music style identification using shallow statistical
descriptors.” volume 37, pages 248{257, March 2007.
[9] J. Foote. “An overview of audio information retrieval.” In
Multimedia Systems, volume 1, pages pp. 307{328, 1999.
[10] Asif Ghias, Jonathan Logan, David Chamberlin, and Brian C.
Smith. “Query by humming: Musical information retrieval in
an audio database.” In Proceedings of the Third ACM
International Conference on Multimedia, MULTIMEDIA `95,
pages 231{236, New York, NY, USA, 1995. ACM.
[11] GeorgeV. Karkavitsas and GeorgeA. Tsihrintzis. “Automatic
music genre classification using hybrid genetic algorithms”.
In GeorgeA. Tsihrintzis, Maria Virvou, Lakhmic. Jain, and
RobertJ. Howlett, editors, Intelligent Interactive Multimedia
Systems and Services, volume 11 of Smart Innovation,
Systems and Technologies, pages 323{335, 2011.
[12] Kunio Kashino, Kazuhiro Nakadai, Tomoyoshi Kinoshita,
and Hidehiko Tanaka. “Organization of hierarchical
perceptual sounds: Music scene analysis with autonomous
processing modules and a quantitative information
integration mechanism.” In Proc. International Joint Conf. on
Arti_cial Intelligence, pages 158{164, 1995.
[13] T Kohonen. Self-organizingmaps.
[14] Tao Li and Mitsunori Ogihara. “Music artist style
identification by semi-supervised learning from both lyrics
and content.” In Proceedings of the 12th Annual ACM
International Conference on Multimedia, MULTIMEDIA `04,
pages 364{367, New York, NY, USA, 2004. ACM.
[15] H. B. Lincoln. “Some criteria and techniques for developing
computerized thematic indices”. In Computers and the
Humanities, volume 2, pages pp. 71{93. Springer, 1967.
[16] Michael Mandel, Michael I. M, and Daniel Ellis. “Song-level
features and support vector machines for music
classification.” 2005.
[17] Matthias Mauch, Simon Dixon, Christopher Harte, and
Queen Mary. Discovering chord idioms through beatles and
real book songs. 2007.
[18] Martin Mckinney and Jeroen Breebaart. “Features for audio
and music classification. In Proceedings of the International
Symposium on Music Information Retrieval,” pages 151{158,
2003.
[19] Mitsunori Ogihara and Tao Li. Ismir 2008 session 5d mir
methods n-gram chord profiles for composer style
representation.
[20] Elias Pampalk, Simon Dixon, and Gerhard Widmer.
Exploring music collections by browsing different views.
volume 28, pages 49{62, Cambridge, MA, USA, June 2004.
[21] Yuting Qi, J.W. Paisley, and L. Carin. Music analysis using
hidden markov mixture models. volume 55, pages
5209{5224, Nov 2007.
[22] P. E. Hart R. O. Duda and D. G. Stork. Pattern classification.
Hagen Soltau, T. Schultz, M. Westphal, and Alex Waibel.
Recognition of music types. In Acoustics, Speech and Signal
Processing, 1998. Proceedings of the 1998 IEEE
International Conference on, volume 2, pages 1137{1140
vol.2, May 1998.
[23] J. Kangas J. Laaksonen T. Kohonen, J. Hynninen. Som pak:
The self-organizing map program package.
[24] B. Whitman, G. Flake, and S. Lawrence. Artist detection in
music with minnowmatch. In Neural Networks for Signal
Processing XI, 2001. Proceedings of the 2001 IEEE Signal
Processing Society Workshop, pages 559{568, 2001}
Keywords
Gaussian mixture models, melodic
contour, music similarity, n-grams, style.