Rapid prototyping: using Wizard of Oz to emulate machine learning features for interactive artistic applications
Resumo
In this paper, we present using the Wizard of Oz method to rapid prototyping machine learning features in interactive artistic applications. Machine learning systems often require time and resources until they were available to be executed. But in the era of agile movement where we have to test as soon as possible and in artistic solutions whose scope initially perhaps be unclear, there is a need for a way to fast testing hypothesis. We also briefly described Ama, a system for adult ballet training at home, which served as a proof of concept of our strategy.
Palavras-chave:
Movement and Gesture, Real-time Interactive Systems
Referências
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Vassileios Balntas, Andreas Doumanoglou, Caner Sahin, Juil Sock, Rigas Kouskouridas, and Tae-Kyun Kim. Pose guided rgbd feature learning for 3d object pose estimation. In Proceedings of the IEEE international conference on computer vision, pages 3856–3864, 2017.
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Antonio Camurri, Gualtiero Volpe, Stefano Piana, Maurizio Mancini, Radoslaw Niewiadomski, Nicola Ferrari, and Corrado Canepa. The dancer in the eye: towards a multilayered computational framework of qualities in movement. In Proceedings of the 3rd International Symposium on Movement and Computing, pages 1–7, 2016.
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Elizabeth Gibbons. Teaching dance: The spectrum of styles. AuthorHouse, 2007.
Pichao Wang, Wanqing Li, Philip Ogunbona, Zhimin Gao, and Hanling Zhang. Mining mid-level features for action recognition based on effective skeleton representation. In 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8. IEEE, 2014.
Aized Amin Soofi and Arshad Awan. Classification techniques in machine learning: applications and issues. Journal of Basic and Applied Sciences, 13:459–465, 2017.
Tone Bratteteig and Guri Verne. Does ai make pd obsolete? exploring challenges from artificial intelligence to participatory design. In Proceedings of the 15th Participatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial-Volume 2, pages 1–5, 2018.
Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. Ux design innovation: Challenges for working with machine learning as a design material. In Proceedings of the 2017 chi conference on human factors in computing systems, pages 278–288, 2017.
Shelly Knotts and Nick Collins. A survey on the uptake of music ai software. In Proceedings of the International Conference on New Interfaces for Musical Expression, pages 499–504, 2020.
McLean J Macionis and Ajay Kapur. Where is the quiet: Immersive experience design using the brain, mechatronics, and machine learning. In NIME, pages 335–338, 2019.
Margaux Fourie and Dustin van der Haar. Ballet pose recognition: A bag-of-words support vector machine model for the dance training environment. In International Conference on Information Science and Applications, pages 317–325. Springer, 2018.
Tom M Mitchell et al. Machine learning. 1997.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
Eduardo Fonseca, Jordi Pons Puig, Xavier Favory, Frederic Font Corbera, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra. Freesound datasets: a platform for the creation of open audio datasets. In Hu X, Cunningham SJ, Turnbull D, Duan Z, editors. Proceedings of the 18th ISMIR Conference; 2017 oct 23-27; Suzhou, China.[Canada]: International Society for Music Information Retrieval; 2017. p. 486-93. International Society for Music Information Retrieval (ISMIR), 2017.
Francisco Bernardo, Michael Zbyszynski, Mick Grierson, and Rebecca Fiebrink. Designing and evaluating the usability of a machine learning api for rapid prototyping music technology. Frontiers in Artificial Intelligence, 3(13):1–18, 2020.
D.M Gavrila. The visual analysis of human movement: A survey. Computer Vision and Image Understanding, 73(1):82–98, 1999.
Basura Fernando, Elisa Fromont, and Tinne Tuytelaars. Mining mid-level features for image classification. International Journal of Computer Vision, 108(3):186–203, 2014.
Vassileios Balntas, Andreas Doumanoglou, Caner Sahin, Juil Sock, Rigas Kouskouridas, and Tae-Kyun Kim. Pose guided rgbd feature learning for 3d object pose estimation. In Proceedings of the IEEE international conference on computer vision, pages 3856–3864, 2017.
Haikuan Wang, Feixiang Zhou, Wenju Zhou, and Ling Chen. Human pose recognition based on depth image multifeature fusion. Complexity, 2018, 2018.
Antonio Camurri, Gualtiero Volpe, Stefano Piana, Maurizio Mancini, Radoslaw Niewiadomski, Nicola Ferrari, and Corrado Canepa. The dancer in the eye: towards a multilayered computational framework of qualities in movement. In Proceedings of the 3rd International Symposium on Movement and Computing, pages 1–7, 2016.
Bill Buxton. Artists and the art of the luthier. ACM SIGGRAPH Computer Graphics, 31(1):10–11, 1997.
Banu Ozkeser. Lean innovation approach in industry 5.0. The Eurasia Proceedings of Science Technology Engineering and Mathematics, (2):422–428, 2018.
Jacob T Browne. Wizard of oz prototyping for machine learning experiences. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–6, 2019.
Yvonne Rogers, Helen Sharp, and Jenny Preece. Interaction design: beyond human-computer interaction. John Wiley & Sons, 2011.
Nils Dahlbäck, Arne Jönsson, and Lars Ahrenberg. Wizard of oz studies—why and how. Knowledge-based systems, 6(4):258–266, 1993.
Elizabeth Gibbons. Teaching dance: The spectrum of styles. AuthorHouse, 2007.
Pichao Wang, Wanqing Li, Philip Ogunbona, Zhimin Gao, and Hanling Zhang. Mining mid-level features for action recognition based on effective skeleton representation. In 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8. IEEE, 2014.
Publicado
24/10/2021
Como Citar
ALELUIA, Alessandra; CABRAL, Giordano.
Rapid prototyping: using Wizard of Oz to emulate machine learning features for interactive artistic applications. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 18. , 2021, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 241-244.
DOI: https://doi.org/10.5753/sbcm.2021.19457.
