A scale-free distribution of false positives for a large class of audio similarity measures

Pattern Recognition

Volumn 41, Issue 1, 2008, Pages 272-284

  Aucouturier, Jean Julien ^a   Pachet, Francois ^b  

^a UNIVERSITY OF TOKYO   (Japan)

^b SONY COMPUTER SCIENCE LABORATORY   (France)

Author keywords

Bag of frames; False positives; Hubs; Mel Frequency Cepstral Coefficients; Music similarity; Timbre

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; SIGNAL ANALYSIS; SPECTRUM ANALYSIS; STATISTICAL METHODS;

FALSE POSITIVES; GAUSSIAN MIXTURE MODELS; MUSIC MODELLING; PARADIGM;

PATTERN RECOGNITION;

EID: 34548048417     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.04.012     Document Type: Article

References (20)

1. Sebastiani F. Machine learning in automated text categorization. ACM Comput. Surv. 34 1 (2002) 1-47
2. Rabiner L., and Juang B. Fundamentals of Speech Recognition (1993), Prentice-Hall, New Jersey
3. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford University Press, Oxford
4. G. Tzanetakis, G. Essl, P. Cook, Automatic musical genre classification of audio signals, in: Proceedings of the ISMIR, 2001.
5. Aucouturier J.-J., Pachet F., and Sandler M. The way it sounds: timbre models for analysis and retrieval of polyphonic music signals. IEEE Trans. Multimedia 7 6 (2005) 1028-1035
6. Aucouturier J.-J., and Pachet F. Improving timbre similarity: how high's the sky?. J. Negative Results Speech Audio Sci. 1 1 (2004)
7. McAdams S., Winsberg S., Donnadieu S., De Soete G., and Krimphoff J. Perceptual scaling of synthesized musical timbres: common dimensions, specificities, and latent subject classes. Psychol. Res. 58 (1995) 177-192
8. Doddington G., Liggett W., Martin A., Przybocki M., and Reynolds D. Sheep, goats, lambs and wolves, a statistical analysis of speaker performance. Proceedings of the Fifth International Conference on Spoken Language Processing (ICSLP). Sydney (Australia) (1998)
9. Hicklin A., Watson C., and Ulery B. The Myth of Goats: How Many People Have Fingerprints That Are Hard to Match? (2005), National Institute of Standards and Technology, USA (Patriot act Report 7271)
10. Kohonen T. Self-Organizing Maps (1995), Springer, Heidelberg
11. Furui S. Speaker-independent isolated word recognition using dynamic features of speech spectrum. IEEE Trans. Speech Audio Process. 34 1 (1986) 52-59
12. Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77 2 (1989)
13. F. Pachet, A. LaBurthe, A. Zils, J.-J. Aucouturier, Popular music access: the sony music browser, J. Am. Soc. Inf. (JASIS), special issue on music information retrieval, 2004.
14. E. Pampalk, Computational models of music similarity and their application to music information retrieval, Doctoral Thesis, Vienna University of Technology, Austria, 2006.
15. Bak P. How Nature Works: The Science of Self-Organized Criticality (1996), Springer, New York
16. Albert R., Jeoung H., and Barabasi A.-L. The diameter of the world wide web. Nature 401 (1999) 130
17. Cano P., Celma O., Koppenberger M., and Martin-Buldu M. Topology of music recommendation networks. Chaos 16 (2006)
18. Lin N., and Zhao H. Are scale-free networks robust to measurement errors?. Bioinformatics 6 (2005) 119
19. J.-J. Aucouturier, B. Defreville, Sounds like a park: A computational technique to recognize soundscapes holistically, without source identification, in: Proceedings of the 19th International Congress on Acoustics (ICA) 2007, Madrid, Spain.
20. T. Kinnunen, I. Krkkinen, P. Frnti, Is speech data clustered?-statistical analysis of cepstral features, in: European Conference on Speech Communication and Technology (EUROSPEECH'2001), Aalborg, Denmark, vol. 4, 2001, pp. 2627-2630.