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Analysis of Recognition of a Musical Instrument in Sound Mixes Using Support Vector Machines

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Języki publikacji
EN
Abstrakty
EN
Experiments with recognition of the dominating musical instrument in sound mixes are interesting from the point of view of music information retrieval, but this task can be very difficult if the mixed sounds are of the same pitch. In this paper, we analyse experiments on recognition of the dominating instrument in mixes of same-pitch sounds of definite pitch. Sound from one octave (no. 4 in MIDI notation) have been chosen, and instruments of various types, including percussive instruments were investigated. Support vector machines were used in our experiments, and statistical analysis of the results was also carefully performed. After discussing the outcomes of these experiments and analyses, we conclude our paper with suggestions regarding directions of possible future research on this subject.
Wydawca
Rocznik
Strony
85--104
Opis fizyczny
Bibliogr. 29 poz., tab.
Twórcy
autor
  • Polish-Japanese Institute of Information Technology Koszykowa 86, 02-008 Warsaw, Poland, alicja@poljap.edu.pl
Bibliografia
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  • [3] Aniola, P., Lukasik, E.: JAVA Library for Automatic Musical Instruments Recognition. AES 122 Conven-tion, Vienna, Austria, 2007.
  • [4] Brown, J.C.: Computer identification of musical instruments using pattern recognition with cepstral coefficients as features. J.Acoust.Soc.Am. 105, 1999, 1933-1941.
  • [5] Chang, C.-C., Lin, C.-J.: LIBSVM: a Library for Support Vector Machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  • [6] Corthaut, N., Govaerts, S., Verbert, K, Duval, E.: Connecting the Dots: Music Metadata Generation, Schemas and Applications. Ninth International Conference on Music Information Retrieval ISMIR 2008.
  • [7] Ferguson, G. A., Takane, Y.: Statistical analysis in psychology and education. 6th ed. McGraw Hill, New York, 1989.
  • [8] Goto M., Hashiguchi H., Nishimura T., Oka R.: RWC Music Database: Music Genre Database and Musical Instrument Sound Database, Proceedings of the 4th International Conference on Music Information Retrieval ISMIR 2003, 229-230.
  • [9] Herrera, P., Amatriain, X., Batlle, E., Serra X.: Towards instrument segmentation for music content description: a critical review of instrument classification techniques. International Symposium on Music Information Retrieval ISMIR, 2000.
  • [10] Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Classification, 2008, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
  • [11] ISO/IEC JTC1/SC29/WG11: MPEG-7 Overview. Available at http://www.chiariglione.org/mpeg/standards/mpeg-7/mpeg-7.htm
  • [12] Jiang, W.: Polyphonic Music Information Retrieval Based on Multi-Label Cascade Classification System. Ph.D thesis, Univ. North Carolina, Charlotte, 2009.
  • [13] Kaminskyj, I.: Multi-feature Musical Instrument Sound Classifier w/user determined generalisation performance. Proceedings of the Australasian Computer Music Association Conference ACMC, 2002, 53-62.
  • [14] M. B. Kursa, E. Kubera,W. R. Rudnicki, A. A.Wieczorkowska: Random Musical Bands Playing in Random Forests. In: M. Szczuka, M. Kryszkiewicz, S. Ramanna, R. Jensen, Q. Hu (Eds.): Rough Sets and Curent Trends in Computing. Proc. RSCTC 2010, Warsaw, Poland, LNAI 6086, Springer-Verlag, 2010, 580-589.
  • [15] Hornbostel, E. M. V., Sachs, C.: Systematik der Musikinstrumente. Ein Versuch. Zeitschrift fur Ethnologie, Vol. 46, No. 4-5, 1914, 553-90.
  • [16] Kitahara, T., Goto, M., Okuno, H.G.: Pitch-Dependent Identification of Musical Instrument Sounds. Applied Intelligence 23, Springer, 2005, 267-275.
  • [17] E. Kubera, A. Wieczorkowska, Z. Ras, M. Skrzypiec: Recognition of Instrument Timbres in Real Polytimbral Audio Recordings. In: J. L. Balcazar, F. Bonchi, A. Gionis, M. Sebag (Eds.): Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II. LNAI 6322, Springer-Verlag, 2010, 97-110.
  • [18] Lowry, R.: Concepts and Applications of Inferential Statistics, http://faculty.vassar.edu/lowry/webtext.html
  • [19] Martin, K.D., Kim, Y.E.: Musical instrument identification: A pattern-recognition approach. 136-th meeting of the Acoustical Society of America, Norfolk, VA, 1998.
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  • [21] Peeters, G., McAdams, S., Herrera, P.: Instrument Sound Description in the Context of MPEG-7. International Computer Music Conference ICMC'2000, 2000.
  • [22] Pett, M.A.: Nonparametric Statistics for Health Care Research. London, Thousand Oaks, New Delhi: Sage Publications, 1997.
  • [23] Platt, J.C.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Microsoft Research, Technical Report MSR-TR-98-14 (1998)
  • [24] Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, New York, 2000.
  • [25] The University of IOWA Electronic Music Studios: Musical Instrument Samples, http://theremin. music.uiowa.edu/MIS.html
  • [26] Wieczorkowska, A.: Towards Musical Data Classification via Wavelet Analysis. In: Ras, Z.W., Ohsuga, S. (eds.): Foundations of Intelligent Systems. Proc. ISMIS'00, Charlotte, NC, USA, LNCS/LNAI, Vol. 1932, Springer-Verlag, 2000, 292-300.
  • [27] Wieczorkowska, A., Kolczyńska, E., Ra´s, Z. W.: Training of Classifiers for the Recognition of Musical Instrument Dominating in the Same-Pitch Mix. In: N. T. Nguyen, R. Katarzyniak (Eds.): New Challenges in Applied Intelligence Technologies. Studies in Computational Intelligence, Volume 134. Springer-Verlag Berlin Heidelberg, 2008, 213-222.
  • [28] Wieczorkowska, A., Kubera, E.: Identification of a dominating instrument in polytimbral same-pitch mixes using SVM classifiers with non-linear kernel. Journal of Intelligent Information Systems, DOI10.1007/s10844-009-0098-3, Springer Netherlands, 2009.
  • [29] Zhang, X.: Cooperative Music Retrieval Based on Automatic Indexing of Music by Instruments and Their Types. Ph.D thesis, Univ. North Carolina, Charlotte, 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0018-0005
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