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An Approach to Automatic Music Band Member Detection Based on Supervised Learning

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Book cover Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation (AMR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7836))

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Abstract

Automatically extracting factual information about musical entities, such as detecting the members of a band, helps building advanced browsing interfaces and recommendation systems. In this paper, a supervised approach to learning to identify and to extract the members of a music band from related Web documents is proposed. While existing methods utilize manually optimized rules for this purpose, the presented technique learns from automatically labelled examples, making therefore also manual annotation obsolete. The presented approach is compared against existing rule-based methods for band-member extraction by performing systematic evaluation on two different test sets.

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References

  1. Agichtein, Y.: Extracting relations from large text collections. Ph.D. thesis, Columbia University, New York, NY, USA (2005)

    Google Scholar 

  2. Alani, H., Kim, S., Millard, D.E., Weal, M.J., Hall, W., Lewis, P.H., Shadbolt, N.R.: Automatic Ontology-Based Knowledge Extraction from Web Documents. IEEE Intelligent Systems 18(1), 14–21 (2003)

    Article  Google Scholar 

  3. Bikel, D.M., Miller, S., Schwartz, R., Weischedel, R.: Nymble: a High-Performance Learning Name-finder. In: Proceedings of the 5th Conference on Applied Natural Language Processing, pp. 194–201 (1997)

    Google Scholar 

  4. Callan, J., Mitamura, T.: Knowledge-Based Extraction of Named Entities. In: Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM 2002), pp. 532–537. ACM, New York (2002)

    Google Scholar 

  5. Casey, M.A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M.: Content-Based Music Information Retrieval: Current Directions and Future Challenges. Proceedings of the IEEE 96, 668–696 (2008)

    Article  Google Scholar 

  6. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A framework and graphical development environment for robust NLP tools and applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL 2002), Philadelphia (July 2002)

    Google Scholar 

  7. Geleijnse, G., Korst, J.: Web-based artist categorization. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, Canada (October 2006)

    Google Scholar 

  8. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics (COLING 1992), vol. 2, pp. 539–545. Association for Computational Linguistics, Stroudsburg (1992)

    Chapter  Google Scholar 

  9. Kim, J.H., Kang, I.H., Choi, K.S.: Unsupervised named entity classification models and their ensembles. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING 2002), vol. 1, pp. 1–7 (2002)

    Google Scholar 

  10. Knees, P.: Text-Based Description of Music for Indexing, Retrieval, and Browsing. Ph.D. thesis, Johannes Kepler Universität, Linz, Austria (November 2010)

    Google Scholar 

  11. Krenmair, A.: Musikspezifische Informationsextraktion aus Webdokumenten. Diplomarbeit, Johannes Kepler University, Linz, Austria (May 2010)

    Google Scholar 

  12. Li, Y., Bontcheva, K., Cunningham, H.: SVM Based Learning System for Information Extraction. In: Winkler, J.R., Niranjan, M., Lawrence, N.D. (eds.) Deterministic and Statistical Methods in Machine Learning. LNCS (LNAI), vol. 3635, pp. 319–339. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  13. Li, Y., Bontcheva, K., Cunningham, H.: Adapting SVM for Data Sparseness and Imbalance: A Case Study on Information Extraction. Natural Language Engineering 15(2), 241–271 (2009)

    Article  Google Scholar 

  14. Li, Y., Shawe-Taylor, J.: The SVM with uneven margins and Chinese document categorization. In: Proceedings of The 17th Pacific Asia Conference on Language, Information and Computation (PACLIC 17), pp. 216–227 (2003)

    Google Scholar 

  15. Schedl, M., Knees, P.: Context-based Music Similarity Estimation. In: Proceedings of the 3rd International Workshop on Learning the Semantics of Audio Signals (LSAS 2009), Graz, Austria (December 2009)

    Google Scholar 

  16. Schedl, M., Knees, P., Widmer, G.: A Web-Based Approach to Assessing Artist Similarity using Co-Occurrences. In: Proceedings of the 4th International Workshop on Content-Based Multimedia Indexing (CBMI 2005), Riga, Latvia (2005)

    Google Scholar 

  17. Schedl, M., Schiketanz, C., Seyerlehner, K.: Country of Origin Determination via Web Mining Techniques. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME 2010): 2nd International Workshop on Advances in Music Information Research (AdMIRe 2010), Singapore, July 19-23 (2010)

    Google Scholar 

  18. Schedl, M., Widmer, G.: Automatically Detecting Members and Instrumentation of Music Bands via Web Content Mining. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds.) AMR 2007. LNCS, vol. 4918, pp. 122–133. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  19. Sekine, S.: NYU: Description of the Japanese NE system used for MET-2. In: Proceedings of the 7th Message Understanding Conference (MUC-7) (1998)

    Google Scholar 

  20. Shavitt, Y., Weinsberg, U.: Songs Clustering Using Peer-to-Peer Co-occurrences. In: Proceedings of the IEEE International Symposium on Multimedia (ISM 2009): International Workshop on Advances in Music Information Research (AdMIRe 2009), San Diego, CA, USA (December 2009)

    Google Scholar 

  21. Slaney, M., White, W.: Similarity Based on Rating Data. In: Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria (September 2007)

    Google Scholar 

  22. Sordo, M., Laurier, C., Celma, O.: Annotating Music Collections: How Content-based Similarity Helps to Propagate Labels. In: Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria, pp. 531–534 (September 2007)

    Google Scholar 

  23. Turnbull, D., Barrington, L., Lanckriet, G.: Five Approaches to Collecting Tags for Music. In: Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA, USA (2008)

    Google Scholar 

  24. Whitelaw, C., Kehlenbeck, A., Petrovic, N., Ungar, L.: Web-scale named entity recognition. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008), pp. 123–132 (2008)

    Google Scholar 

  25. Whitman, B., Lawrence, S.: Inferring Descriptions and Similarity for Music from Community Metadata. In: Proceedings of the 2002 International Computer Music Conference (ICMC 2002), Gothenburg, Sweden, pp. 591–598 (September 2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University, Linz, Austria

    Peter Knees

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  1. Peter Knees
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Editors and Affiliations

  1. Laboratoire d’Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie, 104, Avenue du Président Kennedy, 75016, Paris, France

    Marcin Detyniecki

  2. Departamento de Lenguajes y Sistemas Informáticos (LSI), Universidad Nacional de Educación a Distancia (UNED), 28040, Madrid, Spain

    Ana García-Serrano

  3. Faculty of Computer Science, Otto-von-Guericke University, Universitätsplatz 2, 39106, Magdeburg, Germany

    Andreas Nürnberger  & Sebastian Stober  & 

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Knees, P. (2013). An Approach to Automatic Music Band Member Detection Based on Supervised Learning. In: Detyniecki, M., García-Serrano, A., Nürnberger, A., Stober, S. (eds) Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation. AMR 2011. Lecture Notes in Computer Science, vol 7836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37425-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-37425-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37424-1

  • Online ISBN: 978-3-642-37425-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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