DoodleTunes: Interactive Visual Analysis of Music-Inspired Children Doodles with Automated Feature Annotation

Shuqi Liu, Jia Bu, Huayuan Ye, Juntong Chen, Shiqi Jiang, Mingtian Tao, Liping Guo, Changbo Wang, Chenhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Music and visual arts are essential in children's arts education, and their integration has garnered significant attention. Existing data analysis methods for exploring audio-visual correlations are limited. Yet, relevant research is necessary for innovating and promoting arts integration courses. In our work, we collected substantial volumes of music-inspired doodles created by children and interviewed education experts to comprehend the challenges they encountered in the relevant analysis. Based on the insights we obtained, we designed and constructed an interactive visualization system DoodleTunes. DoodleTunes integrates deep learning-driven methods for automatically annotating several types of data features. The visual designs of the system are based on a four-level analysis structure to construct a progressive workflow, facilitating data exploration and insight discovery between doodle images and corresponding music pieces. We evaluated the accuracy of our feature prediction results and collected usage feedback on DoodleTunes from five domain experts.

Original languageEnglish
Title of host publicationCHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400703300
DOIs
StatePublished - 11 May 2024
Event2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024 - Hybrid, Honolulu, United States
Duration: 11 May 202416 May 2024

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024
Country/TerritoryUnited States
CityHybrid, Honolulu
Period11/05/2416/05/24

Keywords

  • Multimodal data visualization
  • audio data
  • data annotation
  • painting
  • user interface
  • visual analysis

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