| Project/Area Number |
16500051
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
HOSHI Mamoru The University of Electro-Communications, Graduate School of Information Systems, Professor, 大学院情報システム学研究科, 教授 (80125955)
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| Co-Investigator(Kenkyū-buntansha) |
OHMORI Tadashi The University of Electro-Communications, Graduate School of Information Systems, Associate. Professor, 大学院情報システム学研究科, 助教授 (30233274)
KOBAYAKAWA Michihiro The University of Electro-Communications, Graduate School of Information Systems, Assistant Professor, 大学院情報システム学研究科, 助手 (00334582)
ONISHI Kensuke Tokai University, School of Science, Lecturer, 理学部, 講師 (00303024)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2004: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | Music Information Retrieval / Music Retrieval / MPEG-4 / TwinVQ / Music Recommendation / 索引構造 / 楽曲情報抽出 / TwinVQ (MPEG4 Audio) |
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| Research Abstract |
Our purpose of this study is to construct a content-based music retrieval system using coefficients computed in the encoding step of TwinVQ audio compression. In this year, we focus on a content-based music recommendation system and music retrieval system which allows us queries by the covered piece of music.
For constructing music recommendation system, we proposed a framework of content-based music recommendation system. On our framework, we constructed music retrieval functions which allowed us queries by rhythm and timbre. These functions are based on the autocorrelation coefficients and LSP-Cepstrum computed in the encoding step of TwinVQ audio compression. Then, we constructed content-based music recommendation system using the music retrieval functions. Experimental results indicated that our content-based music recommendation system is effective.
For music retrieval function which allows us queries by the covered piece of music, we focus on the autocorrelation coefficients computed in the encoding step of TwinVQ. We evaluated robustness of autocorrelation coefficients with respect to pitch and tempo. Experimental results indicated the robustness with respect to pitch.
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