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Articles

World Stage: Crowdsourcing Paradigm for Expressive Social Mobile Music

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Pages 112-128 | Received 26 Jan 2014, Accepted 20 Nov 2014, Published online: 16 Feb 2015
 

Abstract

The combination of powerful mobile devices and the connective potential of cloud-based computing are changing how, where, and when people use computers. No longer physically tethered, computers embedded in mobile phones and tablets freely roam in daily life alongside their human users while persistently connected to the network. At the same time, the pervasiveness of computing enables us to engage people to solve large-scale problems by leveraging human intelligence and the effect of large crowds. In this paper, we discuss a new model for crowdsourced musical interactions based on mobile devices. This model, which we call World Stage, is a pseudo-anonymous, location-aware ecosystem designed to connect hundreds of thousands of users in a social-musical game involving expressive musical performance and collaborative musical feedback. We describe the motivation and mechanics of the World Stage, and present a full implementation and case study around a commercial iPhone application: Smule’s Leaf Trombone: World Stage. We also present the experiential design and the technological infrastructure around the World Stage and discuss the unique social/musical possibilities it affords. This work is about exploring, perhaps for the very first time, a crowdsourcing eco-system that incorporates expressive music-making and with game-like elements, aimed at inciting a mass audience.

Acknowledgements

This work has been the results of many people working together, and is indebted to Jennifer Wu, David Zhu, Mattias Ljungstrom, Tricia Heath, Arnaud Berry, Jeffery Smith, Mike Earhart, Tony Ventrice, Ryan Pfenninger, Turner Kirk, Jonathan Berger, Elon Berger, Michael Wang, Stefan Kotes, and the entire Smule team. Special thanks to Clara Valenstein for analytics and analysis, David Kerr for support and suggestions, and Gina Collecchia for insights into data analysis and signal processing. We would also like to thank the journal reviewers and editor for their feedback in improving and strengthening this article.

Notes

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