Enabling Acoustic Audience Feedback in Large Virtual Events: Conclusions & Referencesby@feedbackloop
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Enabling Acoustic Audience Feedback in Large Virtual Events: Conclusions & References

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The paper introduces a virtual audience framework for online conferences, addressing synchronization issues and providing real-time audience feedback without audio transmission. Future work includes using deep neural networks for local laughter detection and exploring advanced audio synthesis techniques for improved virtual audience sound.
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(1) Tamay Aykut, Sureel, Palo Alto, California, USA;

(2) Markus Hofbauer, Technical University of Munich, Germany;

(3) Christopher Kuhn, Technical University of Munich, Germany;

(4) Eckehard Steinbach, Technical University of Munich, Germany;

(5) Bernd Girod, Stanford University, Stanford, California, USA.

Abstract & Introduction

Related Works

Virtual Audience Framework

Conclusions & References


In this paper, we presented a virtual audience framework for online conferences. Performers such as actors, comedians, or musicians rely heavily on the feedback of their audience. This work addressed the issue of accumulating noise caused by multiple audio inputs that so far is being solved by requiring the audience to be muted. The proposed virtual audience framework enables all participants to experience the audience feedback without the transmission of an audio stream and the resulting synchronization issues. We collect abstract audience state information, such as the number of clapping and laughing participants, on a central server and synthesize a unified audience sound locally on every client. Every user contributes to the overall audience state and has direct influence on the synthesized audio information.

In future work, reactions such as laughter can be locally detected using methods such as deep neural networks [34] to then generate the abstract audience state data which will be shared with the server. Furthermore, the abstract state information is not restricted to be binary. The field of audio synthesis offers promising ideas such as acoustic unit discovery [35], [36]. The acoustic units present in the acoustic feedback of an audience member can be used as a more informative state data. The joint audience sound can consist of the same abstract units to achieve a sound that closely resembles the actual sound. Such improved synthesis implementations can be easily added to the proposed modular framework to continually improve the virtual audience sound synthesis.


This work has been supported by the Max Planck Center for Visual Computing and Communication.


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This paper is available on arxiv under CC 4.0 license.