Show simple item record

dc.contributor.authorDeng, Daen_NZ
dc.contributor.authorSimmermacher, Christianen_NZ
dc.contributor.authorCranefield, Stephenen_NZ
dc.date.available2011-04-07T03:05:47Z
dc.date.copyright2007-08en_NZ
dc.identifier.citationDeng, D., Simmermacher, C., & Cranefield, S. (2007). A study on feature analysis for musical instrument classification (Information Science Discussion Papers Series No. 2007/04). University of Otago. Retrieved from http://hdl.handle.net/10523/957en
dc.identifier.urihttp://hdl.handle.net/10523/957
dc.description.abstractIn tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper we present an empirical study on feature analysis for classical instrument recognition, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleA study on feature analysis for musical instrument classificationen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages20en_NZ
otago.date.accession2007-08-09en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints718en_NZ
otago.school.eprintsKnowledge, Intelligence & Web Informatics Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.references[1] Y.-H. Tseng, “Content-based retrieval for music collections,” in Proc. of the 22nd ACM SIGIR International Conference on Research and Development in Information Retrieval, 1999, pp. 176–182. [2] B. Kostek, “Musical instrument classification and duet analysis employing music information retrieval techniques,” Proceedings of IEEE, vol. 92, no. 4, pp. 712–729, 2004. [3] G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Transactions on speech and audio processing, vol. 10, pp. 293–302, 2002. [4] T. Lidy and A. Rauber, “Evaluation of feature extractors and psycho-acoustic transformations for music genre classification,” in Proceedings of the 6th Inter. Conf. on Music Information Retrieval, 2005, pp. 34–41. [5] J. Marques and P. Moreno, “A study of musical instrument classification using gaussian mixture models and support vector machines,” Compaq Computer Corporation, Tech. Rep. CRL 99/4, 1999. [6] A. Eronen, “Comparison of features for music instrument recognition,” in Proc. of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2001, pp. 19–22. [7] G. Agostini, M. Longari, and E. Poolastri, “Musical instrument timbres classification with spectral features,” EURASIP Journal on Applied Signal Processing, vol. 2003, no. 1, 2003. [8] S. Essid, G. Richard, and B. David, “Efficient musical instrument recognition on solo performance music using basic features,” in Proceedings of the Audio Engineering Society 25th International Conference, no. 2-5. Audio Engineering Society, 2004, accessed 22.11.2005. [Online]. Available: http://www.tsi.enst.fr/%7Eessid/pub/aes25.pdf [9] J. Foote, “An overview of audio information retrieval,” Multimedia Systems, vol. 7, pp. 2–10, 1999. [10] H. G. Kim, N. Moreau, and T. Sikora, “Audio classification based on MPEG-7 spectral basis representation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 5, pp. 716–725, 2004. [11] J. Aucouturier and F. Pachet, “Scaling up music playlist generation,” in Proc. IEEE International Conference on Multimedia and Expo, vol. 1, 2002, pp. 105 – 108. [12] Z. Xiong, R. Radhakrishnan, A. Divakaran, and T. Huang, “Comparing MFCC and MPEG-7 audio features for feature extraction, maximum likelihood HMM and entropic prior HMM for sports audio classification,” in Proc. of IEEE International Conference on Multimedia and Expo, vol. 3, 2003, pp. 397–400. [13] L. Ma, B. Milner, and D. Smith, “Accoustic environment classification,” ACM Transactions on Speech and Language Processing, vol. 3, no. 2, pp. 1–22, 2006. [14] A. Divakaran, R. Regunathan, Z. Xiong, and M. Casey, “Procedure for audio-assisted browsing of news video using generalized sound recognition,” in Proceedings of SPIE, vol. 5021, 2003, pp. 160–166. [15] ISO/IEC Working Group, “MPEG-7 overview,” URL http://www.chiariglione.org/mpeg/standards/mpeg7/mpeg-7.htm, 2004, accessed 8.2.2007. [16] A. T. Lindsay and J. Herre, “MPEG-7 audio - an overview,” J. Audio Eng. Soc., vol. 49, no. 7/8, pp. 589–594, 2001. [17] G. Peeters, S. McAdams, and P. Herrera, “Instrument sound description in the context of MPEG-7,” in Proc. of International Computer Music Conference, 2000, pp. 166–169. [18] J. C. Brown, O. Houix, and S. McAdams, “Feature dependence in the automatic identification of musical woodwind instruments,” Journal of the Acoustical Society of America, vol. 109, no. 3, pp. 1064–1072, 2001. [19] A. A. Livshin and X. Rodet, “Musical instrument identification in continuous recordings,” in Proceedings of the 7th International Conference on Digital Audio Effects, 2004, pp. 222–226. [20] E. Benetos, M. Kotti, and C. Kotropoulos, “Musical instrument classification using non-negative matrix factorization algorithms and subset feature selection,” in Proceedings of ICASSP 2006, vol. V, 2006, pp. 221–224. [21] I. Kaminskyj and T. Czaszejko, “Automatic recognition of isolated monophonic musical instrument sounds using knnc,” Journal of Intelligent Information Systems, vol. 24, no. 2/3, pp. 199–221, 2005. [22] J. Eggink and G. J. Brown, “Instrument recognition in accompanied sonatas and concertos,” in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. IV, 2004, pp. 217–220. [23] I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. [24] L. Yu and H. Liu, “Efficient feature selection via analysis of relevance and redundancy,” Journal of Machine Learning Research, vol. 5, pp. 1205–1224, 2004. [25] M. Grimaldi, P. Cunningham, and A. Kokaram, “An evaluation of alternative feature selection strategies and ensemble techniques of classifying music,” School of Computer Science and Informatics, Trinity College Dublin, Tech. Rep. TCD-CS-2003-21, 2003. [26] J. Qinlan, C4.5: Programs for machine learning. Morgan Kaufmann, 1993. [27] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical recipes in C. Cambridge University Press, 1988. [28] R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997. [29] J. Tenenbaum, V. de Silva, and J. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science, vol. 290, pp. 2319–2323, 2000. [30] C. Atkeson, A. Moore, and S. Schaal, “Locally weighted learning,” Artificial Intelligence Review, vol. 11, pp. 11–73, 1997. [31] IPEM, “IPEM-toolbox,” URL http://www.ipem.ugent.be/Toolbox, accessed 10/9/2005. [32] M. Slaney, “Auditory-toolbox,” 1998, accessed 22.2.2007. [Online]. Available: http://rvl4.ecn.purdue.edu/malcolm/interval/1998-010 [33] M. Casey, “MPEG-7 sound-recognition tools,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 11, no. 6, pp. 737–747, 2001. [34] I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd ed. Morgan Kaufmann, San Francisco, 2005. [35] K. D. Martin and Y. E. Kim, “Musical instrument identification: a pattern-recognition approach,” Journal of the Acoustical Society of America, vol. 103, no. 3 pt 2, p. 1768, 1998.en_NZ
otago.relation.number2007/04en_NZ
 Find in your library

Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record