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Linked Data Collection and Analysis Platform for Music Information Retrieval

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Semantic Technology (JIST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10055))

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Abstract

There has been extensive research on music information retrieval (MIR), such as signal processing, pattern mining, and information retrieval. In such studies, audio features extracted from music are commonly used, but there is no open platform for data collection and analysis of audio features. Therefore, we build the platform for the data collection and analysis for MIR research. On the platform, we represent the music data with Linked Data, which are in a format suitable for computer processing, and also link data fragments to each other. By adopting the Linked Data, the music data will become easier to publish and share, and there is an advantage that complex music analysis will be facilitated. In this paper, we first investigate the frequency of the audio features used in previous studies on MIR for designing the Linked Data schema. Then, we build a platform, that automatically extracts the audio features and music metadata from YouTube URIs designated by users, and adds them to our Linked Data DB. Finally, the sample queries for music analysis and the current record of music registrations in the DB are presented.

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Notes

  1. 1.

    http://wiki.dbpedia.org/.

  2. 2.

    https://musicbrainz.org/.

  3. 3.

    http://musicontology.com/.

  4. 4.

    http://www.ismir.net/.

  5. 5.

    http://www.sii.co.jp/music/try/metronome/01.html.

  6. 6.

    http://www.kanzaki.com/works/2009/pub/graph-draw.

References

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  2. Wang, M., Kawamura, T., Sei, Y., Nakagawa, H., Tahara, Y., Ohsuga, A.: Context-aware Music Recommendation with Serendipity Using Semantic Relations. In: Proceedings of 3rd Joint International Semantic Technology Conference, pp. 17–32 (2013)

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  3. Osmalskyj, J., Foster, P., Dixon, S., Embrechts, J.J.: Combining features for cover song identification. In: Proceedings of the 16th International Society for Music Information Retrieval Conference, pp. 462–468 (2015)

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  4. Luo, Y.-J., Su, L., Yang, Y.-H., Chi, T.-S.: Real-time music tracking using multiple performances as a reference. In: Proceedings of the 16th International Society for Music Information Retrieval Conference, pp. 357–363 (2015)

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Numbers 16K12411, 16K00419, 16K12533.

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Authors and Affiliations

  1. Graduate School of Information Systems, University of Electro-Communications, Tokyo, Japan

    Yuri Uehara, Takahiro Kawamura, Shusaku Egami, Yuichi Sei, Yasuyuki Tahara & Akihiko Ohsuga

Authors
  1. Yuri Uehara
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  2. Takahiro Kawamura
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  3. Shusaku Egami
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  4. Yuichi Sei
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  5. Yasuyuki Tahara
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  6. Akihiko Ohsuga
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Corresponding author

Correspondence to Yuri Uehara .

Editor information

Editors and Affiliations

  1. Information Technology, Monash University, Melbourne, Victoria, Australia

    Yuan-Fang Li

  2. Computer Science and Technology, Nanjing University, Nanjing, China

    Wei Hu

  3. Computer Science, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  4. University of Huddersfield, Huddersfield, United Kingdom

    Grigoris Antoniou

  5. Information and Communication Technology, Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  6. ISTD, Singapore University of Technology and Design, Singapore, Singapore

    Jun Sun

  7. Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

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© 2016 Springer International Publishing AG

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Cite this paper

Uehara, Y., Kawamura, T., Egami, S., Sei, Y., Tahara, Y., Ohsuga, A. (2016). Linked Data Collection and Analysis Platform for Music Information Retrieval. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-50112-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50111-6

  • Online ISBN: 978-3-319-50112-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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