Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2030112.2030163 | UbiComp |
Keywords | Field | DocType |
reconstructed cough sound,audio-based cough detection,average true positive rate,reconstructed speech,chronic cough,audio stream,false positive rate,reconstructed intelligibly,novel algorithm,cough detection,low-cost microphone,data collection,privacy,quality of life,health,signal processing | False positive rate,Chronic cough,Fidelity,Psychoacoustics,Computer science,Speech recognition,Mobile phone,True positive rate,Microphone,Intelligibility (communication) | Conference |
Citations | PageRank | References |
52 | 3.28 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric C. Larson | 1 | 583 | 30.46 |
| Tien-Jui Lee | 2 | 107 | 5.86 |
| Sean Liu | 3 | 52 | 3.28 |
| Margaret Rosenfeld | 4 | 97 | 7.49 |
| Shwetak N. Patel | 5 | 2967 | 211.74 |