Advertisement

Multimedia Tools and Applications

, Volume 30, Issue 3, pp 331–349 | Cite as

The Cuidado music browser: an end-to-end electronic music distribution system

  • François Pachet
  • Jean-Julien Aucouturier
  • Amaury La Burthe
  • Aymeric Zils
  • Anthony Beurive
Article

Abstract

The IST project Cuidado, which ran from January 2001 to December 2003, produced the first entirely automatic chain for extracting and exploiting musical metadata for browsing music. The Sony CSL laboratory is primarily interested in the context of popular music browsing in large-scale catalogues. First, we are interested in human-centred issues related to browsing “Popular Music.” Popular here means that the music accessed to is widely distributed, and known to many listeners. Second, we consider “popular browsing” of music, i.e., making music accessible to non-specialists (music lovers), and allowing sharing of musical tastes and information within communities, departing from the usual, single user view of digital libraries. This research project covers all areas of the music-to-listener chain, from music description—descriptor extraction from the music signal, or data mining techniques—similarity based access and novel music retrieval methods such as automatic sequence generation, and user interface issues. This paper describes the scientific and technical issues at stake, and the results obtained.

Keywords

Metadata Music browser Similarity Cultural metadata Acoustic metadata Editorial metadata Popular music 

References

  1. 1.
    Allamanche E, Herre J, Helmuth O, Frba B, Kasten T, Cremer M (2001) Content-based identification of audio material using MPEG-7 low level description. In: Proc. of the 2nd International Symposium on Music Information Retrieval, (ISMIR 01), Bloomington, Indiana, USAGoogle Scholar
  2. 2.
    Aucouturier J-J, Pachet F (2002) Scaling up playlist generation. In: Proc. of the IEEE International Conference on Multimedia and Expo (ICME 02), Lauzanne, SwitzerlandGoogle Scholar
  3. 3.
    Aucouturier J-J, Pachet F (2003) Musical genre: a survey. J New Music Res 32:1CrossRefGoogle Scholar
  4. 4.
    Aucouturier J-J, Pachet F (2004) Improving timbre similarity: how high’s the sky? JNRSAS 1(1)Google Scholar
  5. 5.
    Aucouturier J-J, Pachet F, Sandler M (2005, December) The way it sounds: timbre models for structural analysis and retrieval of polyphonic music signals. IEEE Trans Multimedia 7(6):1028–1035CrossRefGoogle Scholar
  6. 6.
    Berenzweig A, Ellis D (2001) Locating singing voice segments within music signals. In: Proc. IEEE Workshop on Applications of Signal Processing to Acoustics and Audio (WASPAA 01), Mohonk, NY, USAGoogle Scholar
  7. 7.
    Cohen W, Fan W (2000) Web-collaborative filtering: recommending music by crawling the web. In Proc. 9th International World Wide Web Conference (WWW9), Amsterdam, The NetherlandsGoogle Scholar
  8. 8.
    Downie S (2003) Toward the scientific evaluation of music information retrieval systems. In: Proc. International Symposium on Music Information Retrieval (ISMIR 03), Baltimore, Maryland, USAGoogle Scholar
  9. 9.
    Herrera P, Serra X, Peeters G (1999) Audio descriptors and descriptors schemes in the context of MPEG-7. In: Proceedings of the International Computer Music Conference (ICMC 99), Beijing, ChinaGoogle Scholar
  10. 10.
    La Burthe A, Pachet F, Aucouturier JJ (2003) Editorial metadata in the Cuidado Music Browser: between universalism and autism. In: Proc. 3rd International Conference of Web Delivering of music (WedelMusic 03), Leeds, UKGoogle Scholar
  11. 11.
    Pachet F (2003) Content management for electronic music distribution: the real issues. Commun ACM 2003Google Scholar
  12. 12.
    Pachet F, Cazaly D (2000). A taxonomy of musical genres. In: Proc. Content-Based Multimedia Information Access (RIAO), Paris, FranceGoogle Scholar
  13. 13.
    Pachet F, Zils A (2003) Evolving automatically high-level music descriptors from acoustic signals. Springer, Berlin Heidelberg New York LNCS, 2771Google Scholar
  14. 14.
    Pachet F, Westerman G, Laigre D (2001) Musical data mining for electronic music distribution. In: Proceedings of First International Conference of Web Delivering of Music (WedelMusic 01), Firenze, ItalyGoogle Scholar
  15. 15.
    Peeters G, Rodet X (2002) Automatically selecting signal descriptors for sound classification. In: Proc. of the International Computer Music Conference (ICMC 02), Goteborg (Sweden)Google Scholar
  16. 16.
    Scheirer ED (1998, January) Tempo and beat analysis of acoustic musical signals. J Acoust Soc Am (JASA) 103(1):588–601CrossRefGoogle Scholar
  17. 17.
    Scheirer E, Slaney M. Construction and evaluation of a robust multifeature speech/music discriminator. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 97), Munich, GermanyGoogle Scholar
  18. 18.
    Tzanetakis G, Perry C (2002, July) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5)Google Scholar
  19. 19.
    Wold E, Blum T, Keislar D, Wheaton J (1996) Content-based classification, search, and retrieval of audio. IEEE Multimed 3(3):27–36CrossRefGoogle Scholar
  20. 20.
    Zils A, Pachet F (2001) Musical mosaicing. In: Proc. of COST-G6 Conference on Digital Audio Effects (DAFX01), Limerick, IrelandGoogle Scholar
  21. 21.
    Zils A, Pachet F. Extracting automatically the perceived intensity of music titles. In: Proc. of the COST-G6 Conference on Digital Audio Effects (DAFX03), London, UKGoogle Scholar
  22. 22.
    Zils A, Pachet F, Delerue O, Gouyon F (2002) Automatic extraction of drum tracks from polyphonic music signals. In: Proc. 2nd International Conference of web Delivering of Music (WedelMusic 02), Darmstadt, GermanyGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • François Pachet
    • 1
  • Jean-Julien Aucouturier
    • 1
  • Amaury La Burthe
    • 1
  • Aymeric Zils
    • 1
  • Anthony Beurive
    • 1
  1. 1.SONY Computer Science LaboratoryParisFrance

Personalised recommendations