Enhancing Sound-Based Anomaly Detection Using Deep Denoising Autoencoder

Traditional sensor-based methods that detect machine malfunctions in industrial environments are often costly and complex; sound-based anomaly detection offers a simpler alternative. Such methods, however, must contend with industrial noise that masks the sound patterns necessary for effective diagn...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE access Ročník 12; s. 84323 - 84332
Hlavní autori:	Kim, Seong-Mok, Soo Kim, Yong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Acoustic signal processing Anomalies Anomaly detection Audio data automation Data models Fault diagnosis Feature extraction Hearing protection Industrial noise Machine learning Machinery Methodology noise Noise levels Noise reduction Rails signal analysis Unsupervised learning
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!