Name
Abstract | ||
|---|---|---|
Lung sound (LS) provides a convenient technique to detect and discriminate the respiratory diseases. However, in realistic environments these sound recording are subject to serious noise contamination which may be addressed by Variational Mode decomposition (VMD). This paper proposes a novel adventitious lung sound extraction method that combines VMD with Kullback-Leibler divergence(KLD) or Jensen- Shannon divergence (JSD). A noisy Lung sound signal is initially decomposed using VMD into its intrinsic mode functions that have limited bandwidth. Further, their probability density function is estimated using kernel density estimator. This method aims to extract the adventitious sound from noisy lung sounds by combining only the relevant modes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TENCON.2019.8929617 | TENCON IEEE Region 10 Conference Proceedings |
Keywords | Field | DocType |
variational mode decomposition,lung sound,Kullback-Leibler divergence,Jensen-Shannon divergence | Kernel (linear algebra),Noise measurement,Pattern recognition,Computer science,Electronic engineering,Bandwidth (signal processing),Adventitious sounds,Artificial intelligence,Sound recording and reproduction,Probability density function,Lung sound,Kernel density estimation | Conference |
ISSN | Citations | PageRank |
2159-3442 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sonia Gupta | 1 | 0 | 0.34 |
| Monika Agrawal | 2 | 0 | 0.34 |
| Desh Deepak | 3 | 0 | 0.34 |