Be the Beat: AI-Powered Boombox for Music Suggestion from Freestyle Dance
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3689050.3705995.pdf
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15.4 MB
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e35ed119317b1737578502ee1992c898
Author(s)
Chang, Ethan
Chen, Zhixing
Labrune, Jb
Coelho, Marcelo
Date Issued
March 4, 2025
Publisher
ACM|Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction
Citation
Ethan Chang, Zhixing Chen, Jb Labrune, and Marcelo Coelho. 2025. Be the Beat: AI-Powered Boombox for Music Suggestion from Freestyle Dance. In Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '25). Association for Computing Machinery, New York, NY, USA, Article 67, 1–6.
Version
Final published version
Abstract
Dance has traditionally been guided by music throughout history and across cultures, yet the concept of dancing to create music is rarely explored. In this paper, we introduce Be the Beat, an AI-powered boombox designed to suggest music from a dancer’s movement. Be the Beat uses PoseNet to describe movements for a large language model, enabling it to analyze dance style and query APIs to find music with similar style, energy, and tempo. In our pilot trials, the boombox successfully matched music to the tempo of the dancer’s movements and even distinguished the intricacies between house and Hip-Hop moves. Dancers interacting with the boombox reported having more control over artistic expression and described the boombox as a novel approach to discovering dance genres and choreographing creatively. Be the Beat creates a space for human-AI collaboration on freestyle dance, empowering dancers to rethink the traditional dynamic between dance and music.
Description
TEI ’25, March 04–07, 2025, Bordeaux / Talence, France
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3689050.3705995
