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

Authors:
Eric C. Larson;TienJui Lee;Sean Liu;Margaret Rosenfeld;Shwetak N. Patel
Affiliations:
University of Washington, Seattle, WA, USA;University of Washington, Seattle, WA, USA;University of Washington, Seattle, WA, USA;Seattle Children's Hospital, Seattle, WA, USA;University of Washington, Seattle, WA, USA
Venue:
Proceedings of the 13th international conference on Ubiquitous computing
Year:
2011

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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.