Advertisement

Extending Sound Sample Descriptions through the Extraction of Community Knowledge

  • Frederic Font
  • Xavier Serra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

Sound and music online services driven by communities of users are filled with large amounts of user-created content that has to be properly described. In these services, typical sound and music modeling is performed using either content-based or context-based strategies, but no special emphasis is given to the extraction of knowledge from the community. We outline a research plan in the context of Freesound.org and propose ideas about how audio clip sharing sites could adapt and take advantage of particular user communities to improve the descriptions of their content.

Keywords

sound and music computing online communities folksonomies emergent semantics freesound 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aberer, K., et al.: Emergent Semantics Principles and Issues. In: Proc. of the International Conference on Database Systems for Advanced Applications, pp. 25–38 (2004)Google Scholar
  2. 2.
    Cano, P., et al.: Knowledge and Content-based Audio Retrieval Using WordNet. In: Proc. of the International Conference on E-business and Telecommunication Networks, pp. 301–308 (2004)Google Scholar
  3. 3.
    Cantador, I., Konstas, I., Jose, J.M.: Categorising Social Tags to Improve Folksonomy-based Recommendations. Web Semantics: Science, Services and Agents on the World Wide Web 9(1), 1–15 (2011)CrossRefGoogle Scholar
  4. 4.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Kleanthous, S., Dimitrova, V.: Analyzing Community Knowledge Sharing Behavior. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 231–242. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Masthoff, J.: Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. User Modeling and User-Adapted Interaction 14(1), 37–85 (2004)CrossRefGoogle Scholar
  7. 7.
    Mika, P.: Ontologies are Us: A Unified Model of Social Networks and Semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5(1), 5–15 (2007)CrossRefGoogle Scholar
  8. 8.
    Roma, G., Herrera, P.: Community Structure in Audio Clip Sharing. In: Proc. of the International Conference on Intelligent Networking and Collaborative Systems, pp. 200–205 (2010)Google Scholar
  9. 9.
    Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A., Bernier, C.: Analysis of Strategies for Building Group Profiles. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 40–51. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Wang, J., Chen, X., Hu, Y., Feng, T.: Predicting High-level Music Semantics using Social Tags via Ontology-based Reasoning. In: Proc. of the International Conference on Music Information Retrieval, pp. 405–410 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Frederic Font
    • 1
  • Xavier Serra
    • 1
  1. 1.Music Technology GroupUniversitat Pompeu FabraBarcelonaSpain

Personalised recommendations