Abstract

Imagine being in a crowded room with a cacophony of speakers and having the ability to focus on or remove speech from a specific 2D region. This would require understanding and manipulating an acoustic scene, isolating each speaker, and associating a 2D spatial context with each constituent speech. However, separating speech from a large number of concurrent speakers in a room into individual streams and identifying their precise 2D locations is challenging, even for the human brain. Here, we present the first acoustic swarm that demonstrates cooperative navigation with centimeter-resolution using sound, eliminating the need for cameras or external infrastructure. Our acoustic swarm forms a self-distributing wireless microphone array, which, along with our attention-based neural network framework, lets us separate and localize concurrent human speakers in the 2D space, enabling speech zones. Our evaluations showed that the acoustic swarm could localize and separate 3-5 concurrent speech sources in real-world unseen reverberant environments with median and 90-percentile 2D errors of 15 cm and 50 cm, respectively. Our system enables applications like mute zones (parts of the room where sounds are muted), active zones (regions where sounds are captured), multi-conversation separation and location-aware interaction.

Want to mute or focus on speech from a specific region in a crowded room? Here, the authors built an acoustic swarm that, along with neural networks, separates and localizes concurrent speakers in the 2D space with high precision.

Details

Title
Creating speech zones with self-distributing acoustic swarms
Author
Itani, Malek 1   VIAFID ORCID Logo  ; Chen, Tuochao 1   VIAFID ORCID Logo  ; Yoshioka, Takuya 2   VIAFID ORCID Logo  ; Gollakota, Shyamnath 1   VIAFID ORCID Logo 

 University of Washington, Paul G. Allen School of Computer Science and Engineering, Seattle, USA (GRID:grid.34477.33) (ISNI:0000 0001 2298 6657) 
 Microsoft, Cloud and AI, Redmond, USA (GRID:grid.419815.0) (ISNI:0000 0001 2181 3404) 
Pages
5684
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2866961471
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.