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Application of Analysis of Variance to Assessment of Influence of Sound Feature Groups on Discrimination between Musical Instruments

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Foundations of Intelligent Systems (ISMIS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5722))

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Abstract

In this paper, the influence of the selected sound features on distinguishing between musical instruments is presented. The features were chosen basing on our previous research. Coherent groups of features were created on the basis of significant features, adding complementary ones according to the parameterization method applied, to constitute small, homogenous groups. Next, we investigate (for each feature group separately) if there exist significant differences between means of these features for the studied instruments. We apply multivariate analysis of variance along with post hoc analysis in the form of homogeneous groups, defined by mean values of the investigated features for our instruments. If a statistically significant difference is found, then the homogenous group is established. Such a group may consist of one instrument (distinguished by this feature), or more (instruments similar wrt. this feature). The results show which instruments can be best discerned by which features.

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References

  1. Bartlett, H., Simonite, V., Westcott, E., Taylor, H.: A comparison of the nursing competence of graduates and diplomates from UK nursing programmes. Journal of Clinical Nursing 9, 369–381 (2000)

    Article  Google Scholar 

  2. Dziubinski, M., Dalka, P., Kostek, B.: Estimation of musical sound separation algorithm effectiveness employing neural networks. J. Int. Inf. Systems 24(2-3), 133–157 (2005)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. ISO/IEC JTC1/SC29/WG11: MPEG-7 Overview, http://www.chiariglione.org/

  5. Itoyama, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Instrument Equalizer for Query-By-Example Retrieval: Improving Sound Source Separation Based on Integrated Harmonic and Inharmonic Models. In: 9th Int. Conf. ISMIR (2008)

    Google Scholar 

  6. Kursa, M., Rudnicki, W., Wieczorkowska, A., Kubera, E., Kubik-Komar, A.: Musical Instruments in Random Forest. In: 18th Int. Symp. ISMIS (2009)

    Google Scholar 

  7. Little, D., Pardo, B.: Learning Musical Instruments from Mixtures of Audio with Weak Labels. In: 9th Int. Conf. on Music Information Retrieval ISMIR (2008)

    Google Scholar 

  8. Logan, B.: Mel Frequency Cepstral Coefficients for Music Modeling. In: International Symposium on Music Information Retrieval MUSIC IR (2000)

    Google Scholar 

  9. Morrison, D.F.: Multivariate statistical methods, 3rd edn. McGraw-Hill, NY (1990)

    MATH  Google Scholar 

  10. Opolko, F., Wapnick, J.: MUMS - McGill University Master Samples. CD’s (1987)

    Google Scholar 

  11. Viste, H., Evangelista, G.: Separation of Harmonic Instruments with Overlapping Partials in Multi-Channel Mixtures. In: IEEE Workshop WASPAA 2003 (2003)

    Google Scholar 

  12. Wieczorkowska, A., Czyzewski, A.: Rough Set Based Automatic Classification of Musical Instrument Sounds. In: International Workshop RSKD. Elsevier, Amsterdam (2003)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Wieczorkowska, A., Kubik-Komar, A.: Application of discriminant analysis to distinction of musical instruments on the basis of selected sound parameters. In: International Conference on Man-Machine Interactions ICMMI (to appear, 2009)

    Google Scholar 

  15. Winer, B.J., Brown, D.R., Michels, K.M.: Statistical principals in experimental design, 3rd edn. McGraw-Hill, New York (1991)

    Google Scholar 

  16. Zhang, X.: Cooperative Music Retrieval Based on Automatic Indexing of Music by Instruments and Their Types. Ph.D thesis, Univ. North Carolina, Charlotte (2007)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Wieczorkowska, A., Kubik-Komar, A. (2009). Application of Analysis of Variance to Assessment of Influence of Sound Feature Groups on Discrimination between Musical Instruments. In: Rauch, J., RaÅ›, 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_32

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  • DOI: https://doi.org/10.1007/978-3-642-04125-9_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04124-2

  • Online ISBN: 978-3-642-04125-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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