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Research on the accumulation and reuse of music operations

Research Project

Project/Area Number 17H01847
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Research Field Entertainment and game informatics 1
Research InstitutionInstitute of Physical and Chemical Research

Principal Investigator

Hamanaka Masatoshi  国立研究開発法人理化学研究所, 革新知能統合研究センター, チームリーダー (30451686)

Project Period (FY) 2017-04-01 – 2021-03-31
Project Status Completed (Fiscal Year 2020)
Budget Amount *help
¥15,730,000 (Direct Cost: ¥12,100,000、Indirect Cost: ¥3,630,000)
Fiscal Year 2020: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2019: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Keywords計算論的音楽理論 / 音楽構造分析 / 音楽理論GTTM / メロディ―モーフィング / Melody Slot Machine / 深層学習 / タイムスパン木 / メロディスロットマシン / 音楽操作 / 蓄積と再利用 / 作曲・編曲操作 / 音楽情報処理 / 再利用 / メロディモーフィング手法 / メロディ切り替え / 映像切り替え / 深層学習(deep learning) / 深層学習(deep learning) / 深層学習 (deep learning)
Outline of Final Research Achievements

We aimed to enable musical novice to perform high-level music operations such as summarization, simplification, and arrangement that musicians have performed on score. Two problems arise when a user wants to make changes to a part of a song. First, it is difficult for musical novice to process as they wish. Second, if the processing is done carelessly, the musical structure will be broken. We have realized the analysis of the music structure using deep learning and the processing of the melody using the extracted structure. As a result, we were able to build and publish a system "Melody Slot Machine" that allows even musical novice to experience operating music, and a music structure analysis tool.

Academic Significance and Societal Importance of the Research Achievements

ゲームや映画では,シーンごとに少しずつ異なっているが,全体的には似通っているメロディが多く必要となる場合がある.これまでは,職業作曲家がメロディのバリエーションを次々と制作する作業を行ってきたが,我々はそれらの作業の一部をAIで置き換えることにより効率化を行い, 作曲家がより芸術性の高い創作に集中できるようにすることを目指している.本研究では,そのプロトタイプとなるシステムを構築し有用性の検証を行った.

Report

(5 results)
  • 2020 Annual Research Report   Final Research Report ( PDF )
  • 2019 Annual Research Report
  • 2018 Annual Research Report
  • 2017 Annual Research Report
  • Research Products

    (14 results)

All 2021 2020 2019 2018 2017 Other

All Journal Article (9 results) (of which Peer Reviewed: 9 results,  Open Access: 3 results) Presentation (4 results) (of which Int'l Joint Research: 2 results,  Invited: 2 results) Remarks (1 results)

  • [Journal Article] Web-based time-span tree editor and analysis database2021

    • Author(s)
      Masatoshi Hamanaka, Yui Isono, Keiji Hirata, Satoshi Tojo
    • Journal Title

      Proceedings of the 17th Sound and Music Computing Conference (SMC2020)

      Volume: 1 Pages: 338-343

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed
  • [Journal Article] “Melody Slot Machine2019

    • Author(s)
      Masatoshi Hamanaka, Takayuki Nakatsuka, Shigeo Morishima
    • Journal Title

      ACM Siggraph2019 Emerging Technologies ET-245

      Volume: 1

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Melody Slot Machine: A Controllable Holographic Virtual Performer2019

    • Author(s)
      Masatoshi Hamanaka
    • Journal Title

      Proceedings of the 27th ACM International Conference on Multimedia (MM’19)

      Volume: 1 Pages: 2468-2477

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Melody Slot Machine: Melody Morphing by Using Time-span Tree of GTTM2019

    • Author(s)
      Masatoshi Hamanaka
    • Journal Title

      proceedings of International Computer Music Conference (ICMC2019)

      Volume: 1

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Proposal of an Annotation Method for Integrating Musical Technique Knowledge Using a GTTM Time-Span Tree2019

    • Author(s)
      Nami Iino, Mayumi Shimada, Takuichi Nishimura, Hideki Takeda, Masatoshi Hamanaka
    • Journal Title

      Proceedings of the 25th International Conference on MultiMedia Modeling (MMM2019)

      Volume: 11295 Pages: 617-627

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Proposal of an Annotation Method for Integrating Musical Technique Knowledge Using a GTTM Time-Span Tree2019

    • Author(s)
      Nami Iino, Mayumi Shimada, Takuichi Nishimura, Hideki Takeda, Masatoshi Hamanaka
    • Journal Title

      Proceedings of the 25th International Conference on MultiMedia Modeling (MMM2019), Lecture Notes in Computer Science (LNCS)

      Volume: 11295 Pages: 616-627

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed
  • [Journal Article] GTTM Database and Manual Time-span Tree Generation Tool2018

    • Author(s)
      Masatoshi Hamanaka, Keiji Hirata, Satoshi Tojo
    • Journal Title

      Proceedings of the 15th Sound and Music Computing Conference (SMC2018)

      Volume: 15 Pages: 462-467

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Applying Melody Morphing Method to Composition2018

    • Author(s)
      Masatoshi Hamanaka, Keiji Hirata, Satoshi Tojo
    • Journal Title

      the 3rd Conference on Computer Simulation of Musical Creativity (CSMC2018)

      Volume: 3

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed
  • [Journal Article] deepGTTM-I&II: Local Boundary and Metrical Structure Analyzer Based on Deep Learning Technique2017

    • Author(s)
      Masatoshi Hamanaka, Keiji Hirata, Satoshi Tojo
    • Journal Title

      Lecture Note in Computer Science, Bridging People and Sound

      Volume: 1 Pages: 3-21

    • Related Report
      2017 Annual Research Report
    • Peer Reviewed
  • [Presentation] インタラクション可能な仮想演奏者2020

    • Author(s)
      浜中雅俊
    • Organizer
      情報処理学会 音楽情報科学研究会 第127回研究会(音学シンポジウム2020)
    • Related Report
      2020 Annual Research Report
    • Invited
  • [Presentation] Melody Slot Machine2020

    • Author(s)
      浜中雅俊, 中塚貴之
    • Organizer
      第3回羽倉賞受賞記念講演会, 最先端表現技術協会
    • Related Report
      2020 Annual Research Report
    • Invited
  • [Presentation] deepGTTM-III: Simultaneous Learning of Grouping and Metrical Structures2017

    • Author(s)
      Masatoshi Hamanaka, Keiji Hirata, Satoshi Tojo
    • Organizer
      13th International Symposium on Computer Music Multidisciplinary Research (CMMR2017)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Polyphonic Music Analysis Database Based on GTTM2017

    • Author(s)
      Masatoshi Hamanaka, Keiji Hirata, Satoshi Tojo
    • Organizer
      2nd Conference on Computer Simulation of Musical Creativity (CSMC2017)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Remarks] GTTM

    • URL

      http://gttm.jp/gttm/

    • Related Report
      2017 Annual Research Report

URL: 

Published: 2017-04-28   Modified: 2022-01-27  

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