Name
Abstract | ||
|---|---|---|
Cough detection and assessment have crucial clinical value for respiratory diseases. Subjective assessments are widely adopted in clinical measurement nowadays, but they are neither accurate nor reliable. An automatic and objective system for cough assessment is strongly expected. Automatic cough detection from audio signal has been studied by peer works. But they are still facing some difficulties like unsatisfactory detection accuracy or lacking large scale validation. In this paper, deep neural networks (DNN) are applied to model acoustic features in cough detection. A two step cough detection system is proposed based on deep neural networks(DNN) and hidden markov model(HMM). The experimental data set contains audio recordings from 20 patients with each recording lasting for about 24 hours. The performances of the newly proposed system were evaluated via sensitivity, specificity, F1 measure and macro average of recall. Different configurations of deep neural networks are evaluated. Experimental results show that many of the DNN configurations outperform Gaussian Mixture Model (GMM) on sensitivity, specificity and F1 measure respectively. On macro average of recall, 13.38% and 22.0% relative error reduction are achieved. The newly proposed system provides better performance and potential capacity for modeling big audio data on the cough detection task. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BIBM.2014.6999220 | BIBM |
Keywords | Field | DocType |
audio signal,deep neural networks,medical signal detection,gmm,diseases,acoustic features,dnn configurations,pneumodynamics,respiratory diseases,hmm,clinical measurement,mixture models,audio recording,gaussian processes,cough detection,audio signal processing,cough assessment,hidden markov models,neural nets,gaussian mixture model,audio recordings,hidden markov model,speech recognition,neural networks,sensitivity,feature extraction,market research | Audio signal,Experimental data,Computer science,Artificial intelligence,Artificial neural network,Pattern recognition,Speech recognition,Feature extraction,Hidden Markov model,Macro,Machine learning,Approximation error,Mixture model | Conference |
ISSN | Citations | PageRank |
2156-1125 | 4 | 0.51 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia-Ming Liu | 1 | 15 | 3.14 |
| Mingyu You | 2 | 10 | 2.31 |
| Zheng Wang | 3 | 4 | 0.51 |
| Guo-Zheng Li | 4 | 368 | 42.62 |
| Xianghuai Xu | 5 | 10 | 1.65 |
| Zhongmin Qiu | 6 | 4 | 0.51 |