Name
Title | ||
|---|---|---|
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma. |
Abstract | ||
|---|---|---|
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of the unsupervised-ADS is to detect unknown anomalous sounds without training data of anomalous sounds. The use of an AE as a normal model is a state-of-the-art technique for the unsupervised-ADS. To decrease the false positive rate (FPR),... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TASLP.2018.2877258 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Keywords | DocType | Volume |
Linear programming,Task analysis,Probability density function,Speech processing,Training data,Feature extraction | Journal | 27 |
Issue | ISSN | Citations |
1 | 2329-9290 | 12 |
PageRank | References | Authors |
0.83 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koizumi Yuma | 1 | 41 | 11.75 |
| Shoichiro Saito | 2 | 13 | 2.88 |
| Hisashi Uematsum | 3 | 12 | 0.83 |
| Kawachi, Y. | 4 | 13 | 1.52 |
| Harada Noboru | 5 | 67 | 25.07 |