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Combining audio content and social context for semantic music discovery

Published: 19 July 2009 Publication History

Abstract

When attempting to annotate music, it is important to consider both acoustic content and social context. This paper explores techniques for collecting and combining multiple sources of such information for the purpose of building a query-by-text music retrieval system. We consider two representations of the acoustic content (related to timbre and harmony) and two social sources (social tags and web documents). We then compare three algorithms that combine these information sources: calibrated score averaging (CSA), RankBoost, and kernel combination support vector machines (KC-SVM). We demonstrate empirically that each of these algorithms is superior to algorithms that use individual information sources.

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Cited By

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  • (2024)Multimodal music datasets? Challenges and future goals in music processingInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00344-613:3Online publication date: 28-Aug-2024
  • (2022)What’s Next? Artists’ Music after Grammy AwardsAmerican Sociological Review10.1177/00031224221103257(000312242211032)Online publication date: 8-Jul-2022
  • (2022)Multimodal representation learning over heterogeneous networks for tag-based music retrievalExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117969207:COnline publication date: 30-Nov-2022
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      cover image ACM Conferences
      SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
      July 2009
      896 pages
      ISBN:9781605584836
      DOI:10.1145/1571941
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 19 July 2009

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      Author Tags

      1. calibrated score averaging
      2. combining data sources
      3. kernel combination svm
      4. music ir
      5. rankboost

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      Cited By

      View all
      • (2024)Multimodal music datasets? Challenges and future goals in music processingInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00344-613:3Online publication date: 28-Aug-2024
      • (2022)What’s Next? Artists’ Music after Grammy AwardsAmerican Sociological Review10.1177/00031224221103257(000312242211032)Online publication date: 8-Jul-2022
      • (2022)Multimodal representation learning over heterogeneous networks for tag-based music retrievalExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117969207:COnline publication date: 30-Nov-2022
      • (2022)A Content-Based Music Recommendation System Using RapidMinerIntelligent Computing Techniques for Smart Energy Systems10.1007/978-981-19-0252-9_36(395-406)Online publication date: 14-Jun-2022
      • (2021)A New Multilabel System for Automatic Music Emotion Recognition2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)10.1109/MetroInd4.0IoT51437.2021.9488537(625-629)Online publication date: 7-Jun-2021
      • (2019)Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systemsPLOS ONE10.1371/journal.pone.021738914:6(e0217389)Online publication date: 7-Jun-2019
      • (2019)Understanding Faceted Search from Data Science and Human Factor PerspectivesACM Transactions on Information Systems10.1145/328410137:2(1-27)Online publication date: 11-Jan-2019
      • (2019)Machine learning for music genre: multifaceted review and experimentation with audiosetJournal of Intelligent Information Systems10.1007/s10844-019-00582-9Online publication date: 27-Nov-2019
      • (2018)Harnessing epoch-based reclamation for efficient range queriesACM SIGPLAN Notices10.1145/3200691.317848953:1(14-27)Online publication date: 10-Feb-2018
      • (2018)Interval-based memory reclamationACM SIGPLAN Notices10.1145/3200691.317848853:1(1-13)Online publication date: 10-Feb-2018
      • Show More Cited By

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