Bowing-Net: Motion Generation for String Instruments Based on Bowing Information

Asuka Hirata, Keitaro Tanaka, Ryo Shimamura, Shigeo Morishima

研究成果: Conference contribution

抄録

This paper presents a deep learning based method that generates body motion for string instrument performance from raw audio. In contrast to prior methods which aim to predict joint position from audio, we first estimate information that dictates the bowing dynamics, such as the bow direction and the played string. The final body motion is then determined from this information following a conversion rule. By adopting the bowing information as the target domain, not only is learning the mapping more feasible, but also the produced results have bowing dynamics that are consistent with the given audio. We confirmed that our results are superior to existing methods through extensive experiments.

本文言語English
ホスト出版物のタイトルSpecial Interest Group on Computer Graphics and Interactive Techniques Conference Posters, SIGGRAPH 2021
出版社Association for Computing Machinery, Inc
ISBN(電子版)9781450383714
DOI
出版ステータスPublished - 2021 8 5
イベントSpecial Interest Group on Computer Graphics and Interactive Techniques Conference: Posters, SIGGRAPH 2021 - Virtual, Online, United States
継続期間: 2021 8 92021 8 13

出版物シリーズ

名前Special Interest Group on Computer Graphics and Interactive Techniques Conference Posters, SIGGRAPH 2021

Conference

ConferenceSpecial Interest Group on Computer Graphics and Interactive Techniques Conference: Posters, SIGGRAPH 2021
国/地域United States
CityVirtual, Online
Period21/8/921/8/13

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 人間とコンピュータの相互作用

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