Name
Title | ||
|---|---|---|
Anomaly Detection Based On An Ensemble Of Dereverberation And Anomalous Sound Extraction |
Abstract | ||
|---|---|---|
To develop a sound-monitoring system for checking machine health, a method for detecting anomalous sounds is proposed. In real environments such as factories, reverberation and background noise are mixed in an observed signal, so detection performance is degraded. It can be expected that detection performance will be improved by using a front-end algorithm for acoustic signal processing such as dereverberation and denoising. However, any algorithm has pros and cons, so it is not possible to choose the best front-end algorithm only. To solve this problem, the proposed method is based on a front-end ensemble consisting of a blind-dereverberation algorithm and multiple anomalous-sound-extraction algorithms. Experimental results indicate that the proposed method improves detection performance significantly. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683702 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
machine health monitoring, anomaly detection, ensemble, dereverberation, anomalous sound extraction | Noise reduction,Signal processing,Anomaly detection,Background noise,Reverberation,Pattern recognition,Computer science,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yohei Kawaguchi | 1 | 25 | 9.48 |
| Ryo Tanabe | 2 | 14 | 4.78 |
| Takashi Endo | 3 | 29 | 7.78 |
| Kenji Ichige | 4 | 0 | 0.68 |
| Koichi Hamada | 5 | 2 | 1.64 |