SVM based instrument and timbre classification

Studies in Classification, Data Analysis, and Knowledge Organization

Volumn , Issue , 2010, Pages 759-766

  Krey, Sebastian ^a   Ligges, Uwe ^b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

K-MEANS CLUSTERING; MATLAB; SPEECH PROCESSING; SUPPORT VECTOR MACHINES;

CLASSIFICATION TASKS; CLUSTERING METHODS; DIFFERENT CLASS; LINEAR PREDICTION; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; MISCLASSIFICATION ERROR; POLYNOMIAL KERNELS; TIMBRE CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 84879566581     PISSN: 14318814     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-642-10745-0_84     Document Type: Conference Paper

References (10)

1. Bischl, B., Wornowizki, M., & Borg, K. (2009). The mlr Package: Machine Learning in R. From http://www.algorithm-forge.com/bischl/mlr/.
2. Davis, K., & Mermelstein, P. (1980). Comparison of parametric representation for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics Speech and Signal Processing, ASSP-28(4), 357-366.
3. Ellis, D. P. W. (2005). PLP and RASTA (and MFCC, and inversion) in Matlab. From http://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/.
4. Hermansky, H. (1990). Perceptual linear predictive (PLP) analysis of speech. Journal of Acoustical Society of America, 87(4), 1738-1752.
5. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2008). A practical guide to support vector classification. Taipei: National Taiwan University. From http://www.csie.ntu.edu.tw/~cjlin.
6. Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2004). kernlab - An S4 package for kernel methods in R. Journal of Statistical Software, 11(9), 1-20. From http://www.jstatsoft.org/v11/i09/.
7. Klapuri, A., & Davy, M. (2006). Signal processing methods for music transcription. New York: Springer.
8. Opolko, F., & Wapnick, J. (1987). McGill University master samples (CDs). Quebec, Canada: McGill University.
9. R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0, from http://www.R-project.org.
10. Roever, C. (2003). Musikinstrumentenerkennung mit Hilfe der Hough-Transformation, Diplomarbeit TU Dortmund, Fakultät Statistik. From http://www.aei.mpg.de/~chroev/publications/RoeverDiplom.pdf.