Abstract
This paper describes automatic classification of predominant musical instrument in sound mixes, using random forests as classifiers. The description of sound parameterization applied and methodology of random forest classification are given in the paper. Additionally, the significance of sound parameters used as conditional attributes is investigated. The results show that almost all sound attributes are informative, and random forest technique yields much higher classification results than support vector machines, used in previous research on these data.
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References
Abdel-Aty, M., Pande, A., Das, A., Knibbe, W.: Assessing Safety on Dutch Freeways with Data from Infrastructure-Based Intelligent Transportation Systems. Transp. Res. Rec. 2083, 153–161 (2008)
Bureau, A., Dupuis, J., Falls, K., Lunetta, K., Hayward, B., Keith, T., Eerdewegh, P.: Identifying SNPs Predictive of Phenotype Using Random Forests. Gen. Epidem. 28 (2005)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001), http://www.stat.berkeley.edu/~breiman/RandomForests/cc_papers.htm
Carr, D.A., Lach-Hab, M., Yang, S.J., Vaisman, I.I., Blaisten-Barojas, E.: Machine learning approach for structure-based zeolite classification. Micropor. Macropor. Mat. 117, 339–349 (2009)
Cosi, P., De Poli, G., Lauzzana, G.: Auditory Modelling and Self-Organizing Neural Networks for Timbre Classification. J. New Music Research 23, 71–98 (1994)
Diaz-Uriarte, R., Alvarez de Andres, S.: Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3 (2006)
Dziubinski, M., Dalka, P., Kostek, B.: Estimation of musical sound separation algorithm effectiveness employing neural networks. J. Intel. Inf. Syst. 24(2-3), 133–157 (2005)
Fujinaga, I., McMillan, K.: Realtime recognition of orchestral instruments. In: Proceedings of the International Computer Music Conference, pp. 141–143 (2000)
Goto, M.: A real-time music-scene-description system: predominant-f0 estimation for detecting melody and bass lines in real-world audio signals. ISCA 43(4), 311–329 (2004)
Herrera, P., Amatriain, X., Batlle, E., Serra, X.: Towards instrument segmentation for music content description: a critical review of instrument classification techniques. In: International Symposium on Music Information Retrieval ISMIR (2000)
ISO: MPEG-7 Overview, http://www.chiariglione.org/mpeg/
Kaminskyj, I.: Multi-feature Musical Instrument Classifier. MikroPolyphonie 6 (2000)
Klapuri, A.: Signal processing methods for the automatic transcription of music. Ph.D. thesis, Tampere University of Technology, Finland (2004)
Kursa, M., Jankowski, A., Rudnicki, W.: Boruta – a system for feature selection. In: Nguyen, H.S., Huynh, V.N. (eds.) SCKT-08 Hanoi Vietnam (PRICAI 2008), pp. 122–133 (2009)
Lunetta, K.L., Hayward, L.B., Segal, J., Eerdewegh, P.V.: Screening Large-Scale Association Study Data: Exploiting Interactions Using Random Forests. BMC Genetics 5, 32 (2004)
Martin, K.D., Kim, Y.E.: 2pMU9. Musical instrument identification: A pattern-recognition approach. 136 meeting Acoustical Soc. America, Norfolk, VA (1998)
Opolko, F., Wapnick, J.: MUMS – McGill University Master Samples. CD’s (1987)
Rudnicki, W., Kierczak, M., Koronacki, J., Komorowski, J.: A Statistical Method for Determining Importance of Variables in an Information System. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 557–566. Springer, Heidelberg (2006)
Strobl, C., Boulesteix, A., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics 8, 25 (2007)
Strobl, C., Zeileis, A.: Danger: High Power! – Exploring the Statistical Properties of a Test for Random Forest Variable Importance. Tech. Rep.17. Univ. Munich (2008)
Strobl, C., Boulesteix, A.-L., Kneib, T., Augistin, T., Zeileis, A.: Conditional Variable Importance for Random Forests. Tech. Rep. 23. Dept. Stat., Univ. of Munich (2008)
Ward, M.M., Pajevic, S., Dreyfuss, J., Malley, J.D.: Short-Term Prediction of Mortality in Patients with Systemic Lupus Erythematosus: Classification of Outcomes Using Random Forests. Arthritis and Rheumatism 55, 74–80 (2006)
Wieczorkowska, A., Kubera, E., Kubik-Komar, A.: Analysis of Recognition of a Musical Instrument in Sound Mixes Using Support Vector Machines. In: Nguyen, H.S., Huynh, V.N. (eds.) SCKT 2008 Hanoi, Vietnam (PRICAI 2008), pp. 110–121 (2008)
Wieczorkowska, A.: Rough Sets as a Tool for Audio Signal Classification. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS (LNAI), vol. 1609. Springer, Heidelberg (1999)
Xie, Y.Y., Li, X., Ngai, E.W.T., Ying, W.Y.: Customer churn prediction using improved balanced random forests. Expert Syst. Appl. 36, 5445–5449 (2009)
Zhang, X.: Cooperative Music Retrieval Based on Automatic Indexing of Music by Instruments and Their Types. Ph.D thesis, Univ. North Carolina, Charlotte (2007)
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Kursa, M., Rudnicki, W., Wieczorkowska, A., Kubera, E., Kubik-Komar, A. (2009). Musical Instruments in Random Forest. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_31
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DOI: https://doi.org/10.1007/978-3-642-04125-9_31
Publisher Name: Springer, Berlin, Heidelberg
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