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| Content Provider | ACM Digital Library |
|---|---|
| Author | Rosenfeld, Margaret Liu, Sean Patel, Shwetak N. Lee, TienJui Larson, Eric C. |
| 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. |
| Starting Page | 375 |
| Ending Page | 384 |
| Page Count | 10 |
| File Format | PDF QT / MOV |
| ISBN | 9781450306300 |
| DOI | 10.1145/2030112.2030163 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-09-17 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Privacy Health Mobile phones Signal processing Sensing Cough detection |
| Content Type | Video Text |
| Resource Type | Article |
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