Abstract
We extend an approach to search inside large-scale music collections by enabling the user to give feedback on the retrieved music pieces. In the original approach, a search engine that can be queried through free-form natural language text is automatically built upon audio-based and Web-based similarity measures. Features for music pieces in the collection are derived automatically by retrieving relevant Web pages via Google queries and using the contents of these pages to construct term vectors. The additional use of information about acoustic similarity allows for reduction of the dimensionality of the vector space and characterization of audio pieces with no associated Web information. With the incorporation of relevance feedback, the retrieval of pieces can be adapted according to the preferences of the user and thus compensate for inadequately represented initial queries. The approach is evaluated on a collection comprising about 12,000 pieces by using semantic tags provided by Audioscrobbler and a user study which also gives further insights into users search behaviors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Knees, P., Pohle, T., Schedl, M., Widmer, G.: A Music Search Engine Built upon Audio-based and Web-based Similarity Measures. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR 2007), Amsterdam, The Netherlands (2007)
Maddage, N.C., Li, H., Kankanhalli, M.S.: Music structure based vector space retrieval. In: SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (2006)
Ghias, A., Logan, J., Chamberlin, D., Smith, B.C.: Query by humming: musical information retrieval in an audio database. In: Proceedings of the 3rd ACM International Conference on Multimedia (MULTIMEDIA 1995), San Francisco, California, United State (1995)
Baumann, S., Klüter, A., Norlien, M.: Using natural language input and audio analysis for a human-oriented MIR system. In: Proceedings of the 2nd International Conference on Web Delivering of Music (WEDELMUSIC 2002), Darmstadt, Germany (2002)
Celma, O., Cano, P., Herrera, P.: Search Sounds: An audio crawler focused on weblogs. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, B.C., Canada (2006)
Celino, I., Della Valle, E., Cerizza, D., Turati, A.: Squiggle: A semantic search engine for indexing and retrieval of multimedia content. In: Proceedings of the 1st International Workshop on Semantic-enhanced Multimedia Presentation Systems (SEMPS 2006), Athens, Greece (2006)
Knopke, I.: AROOOGA: An audio search engine for the World Wide Web. In: Proceedings of the 2004 International Computer Music Conference (ICMC 2004), Miami, USA (2004)
Whitman, B., Lawrence, S.: Inferring Descriptions and Similarity for Music from Community Metadata. In: Proceedings of the 2002 International Computer Music Conference (ICMC 2002), Gotheborg, Sweden, pp. 591–598 (2002)
Knees, P., Pampalk, E., Widmer, G.: Artist Classification with Web-based Data. In: Proceedings of 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona, Spain, pp. 517–524 (2004)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Aucouturier, J.J., Pachet, F., Sandler, M.: The Way It Sounds: Timbre Models for Analysis and Retrieval of Music Signals. IEEE Transactions on Multimedia 7(6), 1028–1035 (2005)
Pampalk, E.: Computational Models of Music Similarity and their Application to Music Information Retrieval. PhD thesis, Vienna University of Technology (2006)
Aucouturier, J.J., Pachet, F.: Music Similarity Measures: What’s the Use? In: Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002), Paris, France, IRCAM, pp. 157–163 (2002)
Mandel, M., Ellis, D.: Song-Level Features and Support Vector Machines for Music Classification. In: Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK (2005)
Aucouturier, J.J.: Ten Experiments on the Modelling of Polyphonic Timbre. PhD thesis, University of Paris 6 (2006)
Pohle, T., Knees, P., Schedl, M., Widmer, G.: Automatically Adapting the Structure of Audio Similarity Spaces. In: Proceedings of 1st Workshop on Learning the Semantics of Audio Signals (LSAS 2006), Athens, Greece (2006)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Fisher, D.H. (ed.) Proceedings of ICML 1997, 14th International Conference on Machine Learning, Nashville, US, pp. 412–420. Morgan Kaufman Publishers, San Francisco, US (1997)
Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System – Experiments in Automatic Document Processing, Prentice Hall Inc., Englewood Cliffs, NJ, USA (1971)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading, Massachusetts (1999)
Harman, D.: Relevance Feedback Revisited. In: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 1992), Copenhagen, Denmark (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Knees, P., Widmer, G. (2008). Searching for Music Using Natural Language Queries and Relevance Feedback. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds) Adaptive Multimedia Retrieval: Retrieval, User, and Semantics. AMR 2007. Lecture Notes in Computer Science, vol 4918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79860-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-79860-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79859-0
Online ISBN: 978-3-540-79860-6
eBook Packages: Computer ScienceComputer Science (R0)