research-article

Accurate and privacy preserving cough sensing using a low-cost microphone

Authors:

Eric C. Larson,

Margaret Rosenfeld,

Shwetak N. PatelAuthors Info & Claims

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

Pages 375 - 384

https://doi.org/10.1145/2030112.2030163

Published: 17 September 2011 Publication History

Abstract

Audio-based cough detection has become more pervasive in recent years because of its utility in evaluating treatments and the potential to impact the quality of life for individuals with chronic cough. We critically examine the current state of the art in cough detection, concluding that existing approaches expose private audio recordings of users and bystanders. We present a novel algorithm for detecting coughs from the audio stream of a mobile phone. Our system allows cough sounds to be reconstructed from the feature set, but prevents speech from being reconstructed intelligibly. We evaluate our algorithm on data collected in the wild and report an average true positive rate of 92% and false positive rate of 0.5%. We also present the results of two psychoacoustic experiments which characterize the tradeoff between the fidelity of reconstructed cough sounds and the intelligibility of reconstructed speech.

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Index Terms

Accurate and privacy preserving cough sensing using a low-cost microphone
1. Human-centered computing

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Published In

cover image ACM Conferences

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

September 2011

668 pages

ISBN:9781450306300

DOI:10.1145/2030112

General Chairs:
James Landay
University of Washington, USA & Microsoft Research Asia, China
,
Yuanchun Shi
Tsinghua University, China
,
Program Chairs:
Donald J. Patterson
University of California, Irvine, USA
,
Yvonne Rogers
Open University, UK
,
Xing Xie
Microsoft Research Asia, China

Copyright © 2011 ACM.

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Published: 17 September 2011

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Ubicomp '11: The 2011 ACM Conference on Ubiquitous Computing

September 17 - 21, 2011

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https://doi.org/10.2196/38372
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Gharib STran MLuong DDrossos KVirtanen T(2024)Adversarial Representation Learning for Robust Privacy Preservation in AudioIEEE Open Journal of Signal Processing10.1109/OJSP.2023.33491135(294-302)Online publication date: 2024
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