Abstract
This study uses Genetic Programming (GP) in developing a classifier to distinguish between five musical instruments. Using only simple arithmetic and boolean operators with 95 features as terminals, a program is developed that can classify 300 unseen samples with an accuracy of 94%. The experiment is then run again using only 14 of the most often chosen features. Limiting the features in this way raised the best classification to 94.3% and the average accuracy from 68.2% to 75.67%. This demonstrates that not only can GP be used to create a classifier but it can be used to determine the best features to choose for accurate musical instrument classification, giving an insight into timbre.
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References
ANSI: American National Standard-Psychoacoustical Terminology. American National Standards Institute, New York (1973)
Brown, J.: Computer identification of musical instruments using pattern recognition with cepstra coefficients as features. Journal of Acoustical Society of America 105(3) (1999)
Eronen, A.: Comparison of features for musical instrument recognition. In: Workshop on Application of Signal Processing to Audio and Acoustics, New York, pp. 19–22 (2001)
Eronen, A., Klapuri, A.: Musical instrument recognition using cepstral coefficients and temporal features. In: ICASSP (2000)
Essid, S., Richard, G., David, B.: Musical instrument recognition by pairwise classification strategies. IEEE Transaction on Audio, Speech and Language Processing 14, 1401–1412 (2006)
Fraser, A., Fujinaga, I.: Toward real-time recognition of acoustic musical instruments. In: ICMC, China, pp. 175–177 (1999)
Goto, M.: Development of the RWC music database. In: Proceedings of the 18th Congress on Acoustics (2004)
Grey, J.: Multidimensional perceptual scaling of musical timbres. Journal of Acoustical Society of America 61, 1270–1277 (1977)
Herrera, P., Amatriain, X., Batlle, E., Serra, X.: Towards instrument segmentation for music content description: A critical review of instrument classification techniques. In: ISMIR, Plymouth, Massachusetts (2000)
Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24, 417–441, 498–520 (1933)
Kaminskyj, I., Materka, A.: Automatic source identification of monophonc musical instrument sounds. In: IEEE International Conference on Neural Networks, pp. 189–194 (1995)
Koza, J.: Genetic evolution and co-evolution of game strategies. In: The International Conference on Game Theory and Its Applications. Stony Brook, New York (1992)
Loughran, R.: Musical Instrument Identification with Feature Selection Using Evolutionary Methods. Ph.D. thesis, University of Limerick (2009)
Loughran, R., Walker, J., O’Neill, M.: An exploration of genetic algorithms for efficient musical instrument identification. In: Irish Signals and Sytems Conference, Dublin, Ireland (2009)
Loughran, R., Walker, J., O’Neill, M., O’Farrell, M.: Comparison of features for musical instrument identification using artificial neural networks. In: CMMR, Copenhagen, Denmark, pp. 19–33 (2008)
MATLAB7: Version 7.2 (r2006a) matlab software. In: The Mathworks (2006)
Opolko, F., Wapnick, J.: McGill university master samples (MUMS) (cds) (1987)
Silva, S.: GPLAB - a genetic programming toolbox for MATLAB (2004), http://gplab.sourceforge.net/
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Loughran, R., Walker, J., O’Neill, M., McDermott, J. (2012). Genetic Programming for Musical Sound Analysis. In: Machado, P., Romero, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2012. Lecture Notes in Computer Science, vol 7247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29142-5_16
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DOI: https://doi.org/10.1007/978-3-642-29142-5_16
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