References
- Aucouturier , J. -J. and Pachet , F. . Music similarity measures: what's the use? . Proceedings of the Third International Conference on Music Information Retrieval (ISMIR'02) . pp. 157 – 163 .
- Buyoli , C. L. and Loureiro , R. , eds. . Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004) . Barcelona : Audiovisual Institute, Pompeu Fabra University .
- Cohen , P. R. 1995 . Empirical Methods for Artificial Intelligence , Cambridge, MA : MIT Press .
- Dietterich , T. G. 1998 . Approximate statistical tests for comparing supervised classification learning algorithms . Neural Computation , 10 ( 7 ) : 1895 – 1924 .
- Downie , J. S. 2004 . The scientific evaluation of music information retrieval systems: foundations and future . Computer Music Journal , 28 ( 2 ) : 12 – 23 .
- Efron , B. 1982 . The Jackknife, the Bootstrap, and Other Resampling Plans , SIAM CBMS-NSF Monograph 38, Philadelphia .
- Egmont-Petersen , M. , Talmon , J. L. , Brender , J. and McNair , P. 1994 . On the quality of neural net classifiers . Artificial Intelligence in Medicine , 6 : 359 – 381 .
- Everitt , B. S. 1977 . The Analysis of Contingency Tables , London : Chapman & Hall .
- Feelders , A. and Verkooijen , W. . Which method learns most from the data? . Proceedings of the Fifth International Workshop on AI and Statistics . January 1995 . pp. 219 – 225 . Florida : Fort Lauderdale .
- Flexer , A. 1996 . “ Statistical evaluation of neural network experiments: minimum requirements and current practice ” . In Cybernetics and Systems '96 , Edited by: Trappl , R. 1005 – 1008 . Wien : Oesterreichische Studiengesellschaft fuer Kybernetik .
- Hastie , T. , Tibshirani , R. and Friedman , J. 2001 . The Elements of Statistical Learning , New York/Berlin/Heidelberg : Springer .
- Kibler , D. and Langley , P. 1988 . Machine learning as an experimental science . Machine Learning , 3 ( 1 ) : 5 – 8 .
- Logan , B. and Salomon , A. . A music similarity function based on signal analysis . Proceedings of the IEEE International Conference on Multimedia and Expo . Tokyo, Japan. pp. 190
- Michie , D. , Spiegelhalter , D. J. and Taylor , C. C. , eds. 1994 . Machine Learning, Neural and Statistical Classification , England : Ellis Horwood .
- Mosteller , F. and Tukey , J. W. 1977 . Data Analysis and Regression – A Second Course in Statistics , Reading, MA : Addison-Wesley .
- Pampalk , E. 2004 . “ A Matlab Toolbox to compute music similarity from audio ” . Edited by: Buyoli and Loureiro . 254 – 257 . (2004)
- Pampalk , E. , Flexer , A. and Widmer , G. . Improvements of audio-based music similarity and genre classification . Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR'05) . September 11 – 15 , London, UK. pp. 628 – 633 .
- Prechelt , L. 1996 . A quantative study of experimental evaluations of neural network learning algorithms: current research practice . Neural Networks , 9 ( 3 ) : 457 – 462 .
- Ripley , B. D. 1992 . Statistical Aspects of Neural Networks , Oxford : Department of Statistics, University of Oxford .
- Salzberg , S. 1997 . On comparing classifiers: pitfalls to avoid and a recommended approach . Data Mining and Knowledge Discovery , 1 ( 3 ) : 317 – 328 .
- Siegel , S. 1956 . Nonparametric Statistics for the Behavioral Sciences , Tokyo : McGraw-Hill .
- Tzanetakis , G. and Cook , P. 2002 . Musical genre classification of audio signals . IEEE Transactions on Speech and Audio Processing , 10 ( 5 ) : 293 – 302 .