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RIKEN Center for Advanced Intelligence Project Music Information Intelligence Team

Team Director: Masatoshi Hamanaka (D.Eng.)

Research Summary

Masatoshi Hamanaka(D.Eng.)

Music Information Intelligence Team will develop a computational theory in which media operations are expressed as combinations of lattice operations. We will construct a system that accumulates media operation cases of media design experts and lets novices reuse them to produce content. We also conduct interdisciplinary research on drone and drug discovery.

Main Research Fields

  • Computer Science

Related Research Fields

  • Engineering
  • Multidisciplinary

Research Subjects

  • Music Information Science
  • Drone
  • Drug discovery

Research Subjects

  • Music Information Science
  • Drone
  • Drug discovery

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

  • 1.Hamanaka, M., Hirata, K., and Tojo, S.:
    "deepGTTM-III: Simultaneous Learning of Grouping and Metrical Structures"
    13th International Symposium on Computer Music Multidisciplinary Research (CMMR2017), 2017.
  • 2.Hamanaka, M., Hirata, K., and Tojo, S.:
    "Polyphonic Music Analysis Database based on GTTM"
    2nd Conference on Computer Simulation of Musical Creativity (CSMC2017), 2017.
  • 3.*Hamanaka, M., Taneishi, K., Iwata, H., Ye, J., Pei, J., Hou, J., and Okuno, Y.:
    "CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning"
    Molecular Informatics, Volume 36, Issue 1-2, 2017.
  • 4.*Hamanaka, M., Hirata, K., and Tojo, S.:
    "Implementing Methods for Analyzing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music"
    In Computational Music Analysis, David Meredith (Ed.), pp.221-249, Springer, 2016.
  • 5.*Hamanaka, M., Hirata, K., and Tojo, S.:
    "deepGTTM-II: Automatic Generation of Metrical Structure based on Deep Learning Technique"
    13th Sound and Music Conference (SMC2016), pp.221-249, 2016.
  • 6.*Hamanaka, M., Hirata, K., and Tojo, S.:
    "deepGTTM-I: Local Boundaries Analyzer based on Deep Learning Technique"
    13th International Symposium on Computer Music Multidisciplinary Research (CMMR2016), pp.8-20, 2016.
  • 7.*Hamanaka, M., Taneishi, K., Iwata, H., and Okuno, Y.:
    "Prediction of Compound-Protein Interactions based on Deep Learning"
    Proceeding of The 6th French-Japanese Workshop on Computational Methods in Chemistry, March 2016.
  • 8.*Hamanaka, M., Hirata, K., and Tojo, S.:
    "Sigma GTTM III: Learning based Time-span Tree Generator based on PCFG"
    Proceedings of The 11th International Symposium on Computer Music Multidisciplinary Research (CMMR 2015), pp.303-317, 2015.
  • 9.*Hamanaka, M., Hirata, K., and Tojo, S.:
    "Structural Similarity based on Time-span Sub-trees"
    Proceedings of The 5th International Conference on Mathematics and Computation in Music (MCM2015), pp. 187-192, 2015.
  • 10.*Hamanaka, M., Hirata, K., and Tojo, S.:
    "Musical Structural Analysis Database Based on GTTM"
    Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), pp.325-330, 2014.

Related Links

Lab Members

Principal investigator

Masatoshi Hamanaka
Team Director

Core members

Hiroya Miura
Research Scientist
Shintaro Seki
JSPS PD Researcher
Yui Isono
Technical Staff I
Mayumi Shimada
Technical Staff I
Tetsuya Komuro
Senior Visiting Scientist

Contact Information

Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi,
Chuo-ku, Tokyo
103-0027, Japan
Email: masatoshi.hamanaka [at] riken.jp

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