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OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection
Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive am...
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| Pubblicato in: | Sensors (Basel) |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2021
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8309714/ https://ncbi.nlm.nih.gov/pubmed/34300545 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s21144805 |
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