NSF Project CNS #2032125
Human Sound Localization and Analytics
Synopsis
COVID-19 is spreading at an unprecedented
rate resulting in the death of so many people all over the world. This project
proposes to develop techniques and mobile systems that localize human sound
such as cough and voice and alarm a user when someone is within the social
distance. The goal of the proposed research includes:
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Design algorithms
and systems to localize uncontrolled and unknown human sound. The
multi-resolution analysis will be performed on low-frequency voice signals to
further enhance accuracy.
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Develop
applications that can leverage the sound source location.
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Incorporate the
research outcome in curriculum and outreach.
Personnel
2. Mei
Wang
3. Wei
Sun
Publication
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Localizing
Human Voice. Mei Wang, Wei Sun, Lili Qiu. In Proc. of NSDI 2021.
Abstract: The ability for a smart speaker to localize a user based on
his/her voice opens the door to many new applications. In this paper, we
present a novel system, MAVL, to localize human voice. It consists of three
major components: (i) We first develop a novel multi-resolution
analysis to estimate the AoA of time-varying
low-frequency coherent voice signals coming from multiple propagation paths;
(ii) We then automatically estimate the room structure by emitting acoustic
signals and developing an improved 3D MUSIC algorithm; (iii) We finally
re-trace the paths using the estimated AoA and room
structure to localize the voice. We implement a prototype system using a single
speaker and a uniform circular microphone array. Our results show that it
achieves median errors of 1.49o and 3.33o for the top two AoAs
estimation and achieves median localization errors of 0.31m in line-of-sight (LoS) cases and 0.47m in non-line-of-sight (NLoS) cases.
Outreach
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Talk at IoT
Seminar at MIT Fall 2020
- Keynote speech at Comsnets 2022 January 2022
- Keynote speech at ChinaSys 2022 May 2022