Understandable models of music collections based on exhaustive feature generation with temporal statistics

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2006, Issue , 2006, Pages 882-891

  Moerchen, Fabian ^a   Mierswa, Ingo ^b   Ultsch, Alfred ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

^b TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Feature generation; Genre classification; Logistic re gression; Meta learning; Music mining

Indexed keywords

COMPUTER MUSIC; CUSTOMER SATISFACTION; FEATURE EXTRACTION; MATHEMATICAL MODELS; REGRESSION ANALYSIS; SEMANTICS; STATISTICAL METHODS;

FEATURE GENERATION; GENRE CLASSIFICATIONS; LOGISTIC REGRESSIONS; META LEARNING; MUSIC MINING;

DATA MINING;

EID: 33749540712     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1150402.1150523     Document Type: Conference Paper

References (47)

1. C. C. Aggarwal, A. Hinneburg, and D. A. Keim. On the surprising behavior of distance metrics in high dimensional space. In Proc. Intl. Conf. on Database Theory, pages 420-434, 2001.
2. J.-J. Aucouturier and F. Fachet. Finding songs that sound the same. In Proc. IEEE Benelux Workshop on Model based Processing and Coding of Audio, pages 1-8, 2002.
3. J.-J. Aucouturier and F. Pachet. Improving timbre similarity: How high is the sky? Journal of Negative Results in Speech and Audio Sciences, 1(1):1-13, 2004.
4. J.-J. Aucouturier and F. Pachet. Tools and architecture for the evaluation of similarity measures: case study of timbre similarity. In Proc. ISMIR, 2004.
5. A. Berenzweig, D. Ellis, and S. Lawrence. Anchor space for classification and similarity measurement of music. In Proc. ICME, pages 1-29-32, 2003.
6. M. W. Berry. Survey of Text Mining : Clustering, Classification, and Retrieval. Springer, 2003.
7. T. G. Dietterich. Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, First International Workshop on Multiple Classifier Systems, pages 1-15, 2000.
8. S. Fischer, R. Klinkenberg, I. Mierswa, and O. Ritthoff. Yale: Yet Another Learning Environment - Tutorial. Technical Report CI-136/02, Collaborative Research Center 531, University of Dortmund, Germany, 2002.
9. A. Genkin, D. D. Lewis, and D. Madigan. Large-scale bayesian logistic regression for text categorization. Technical report, DIMACS, 2004.
10. M. Goto. A chorus-section detecting method for musical audio signals. In Proc. IEEE ICASSP, pages 437-440, 2003.
11. G. Guo and S. Z. Li. Content-Based Audio Classification and Retrieval by Support Vector Machines. IEEE Transaction on Neural Networks, 14(1):209-215, 2003.
12. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2001.
13. P. Herrera, J. Bello, G. Widmer, M. Sandler, O. Celma, F. Vignoli, E. Pampalk, P. Cano, S. Pauws, and X. Serra. Simac: Semantic interaction with music audio contents. In Proc. of the 2nd European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies, 2005.
14. H. Homburg, I. Mierswa, B. Moeller, K. Morik, and M. Wurst. A benchmark dataset for audio classification and clustering. In Proc. ISMIR, pages 528-531, 2005.
15. H. Kantz and T. Schreiber. Nonlinear Time Series Analysis. Cambridge University Press, 1997.
16. S. le Cessie and J. van Houwelingen. Ridge estimators in logistic regression. Applied Statistics, 41(1):191-201, 1992.
17. D. Li, I. Sethi, N. Dimitrova, and T. McGee. Classification of general audio data for content-based retrieval. Pattern Recognition Letters, 22:533-544, 2001.
18. T. Li, M. Ogihara, and Q. Li. A comparative study on content-based music genre classification. In Proc. ACM SIGIR, pages 282-289, 2003.
19. B. Logan and A. Salomon. A music similarity function based on signal analysis. In IEEE Intl. Conf. on Multimedia and Expo, page 190, 2001.
20. M. McKinney and J. Breebaart. Features for audio and music classification. In Proc. ISMIR, pages 151-158, 2003.
21. A. Meng, P. Ahrendt, and J. Larsen. Improving music genre classification by short-time feature integration. In Proc. IEEE ICASSP, pages 497-500, 2005.
22. I. Mierswa and K. Morik. Automatic feature extraction for classifying audio data. Machine Learning Journal, 58:127-149, 2005.
23. B. Moore and B. Glasberg. A revision of zwickers loudness model. ACTA Acustica, 82:335-345, 1996.
24. F. Mörchen, A. Ultsch, M. Nöcker, and C. Stamm. Databionic visualization of music collections according to perceptual distance. In Proc. ISMIR, pages 396-403, 2005.
25. F. Mörchen, A. Ultsch, M. Thies, and I. Löhken. Modelling timbre distance with temporal statistics from polyphonic music. IEEE TSAP, 14(1), 2006.
26. F. Mörchen, A. Ultsch, M. Thies, I. Löhken, Nöcker, C. Stamm, N. Efthymiou, and M. Kümmerer. MusicMiner: Visualizing timbre distances of music as topograpical maps. Technical report, CS Dept., Philipps-University Marburg, Germany, 2005.
27. F. Pachet and A. Zils. Evolving automatically high-level music descriptors from acoustic signals. In Proc. Intl. Symposium on Computer Music Modeling and Retrieval, 2003.
28. E. Pampalk. A Matlab toolbox to compute music similarity from audio. In Proc. ISMIR, 2004.
29. E. Pampalk, S. Dixon, and G. Widmer. On the evaluation of perceptual similarity measures for music. In Proc. Intl. Conf. on Digital Audio Effects, pages 6-12, 2003.
30. E. Pampalk, A. Rauber, and D. Merkl. Content-based organization and visualization of music archives. In Proc. ACM Multimedia, pages 570-579, 2002.
31. J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, chapter 12. MIT-Press, 1999.
32. J. R. Quinlan. C4.5: Programs for Machine Learning. Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
33. L. Rabiner and B.-H. Juang. Fundamentals of Speech Recognition. Prentice-Hall, 1993.
34. C. Snoek and M. Worring. Multimodal video indexing: A review of the state-of-the-art. Multimedia Tools and Applications, 25(1):5-35, 2005.
35. R. Stenzel and T. Kamps. Improving content-based similarity measures by training a collaborative model. In Proc. ISMIR 2005, pages 264-271, 2005.
36. F. Takens. Dynamical systems and turbulencs. In D. Rand and L. Young, editors, Lecture Notes in Mathematics, volume 898, pages 366-381. Springer, 1981.
37. R. Tibshirani. Regression shrinkage and selection via the lasso. J. Royal Statistical Soc. B., 58:267-288, 1996.
38. G. Tzanetakis and P. Cook. Marsyas: A framework for audio analysis. Organised Sound, 4(30):169-175, 2000.
39. G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE TSAP, 10(5):293-302, 2002.
40. G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In Proc. ISMIR, pages 205-210, 2001.
41. G. Tzanetakis, G. Essl, and P. Cook. Human perception and computer extraction of beat strength. In Proc. Intl. Conf. on Digital Audio Effects, 2002.
42. A. Ultsch. Self-organizing neural networks for visualization and classification. In Proc. Conf. German Classification Society, 1992.
43. K. West and S. Cox. Features and classifiers for the automatic classification of musical audio signals. In Proc. ISMIR, 2004.
44. D. H. Wolpert. Stacked generalization. Neural Networks, 5:241-259, 1992.
45. C. Xu, N. Maddage, and X. Shao. Musical genre classification using support vector machines. In Proc. IEEE ICASSP, pages 429-432, 2003.
46. T. Zhang and C. Kuo. Content-based Classification and Retrieval of Audio. In Conf. on Advanced Signal Processing Algorithms, Architectures, and Implementations VIII, 1998.
47. E. Zwicker and S. Stevens. Critical bandwidths in loudness summation. The Journal of the Acoustical Society of America, 29(5):548-557, 1957.