Anomaly Detection in Industrial Machine Sounds Using High-Frequency Features and Gate Recurrent Unit Networks

Detecting anomalies in industrial sound is critical for maintaining operational efficiency, preventing costly equipment failures, and ensuring workplace safety. However, it presents significant challenges due to the complexity and variability of industrial environments, including background noise an...

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Główni autorzy:	Thi-Thu-Huong Le, Andro Aprila Adiputra, Jiwon Yun, Howon Kim
Format:	Artigo
Język:	Inglês
Wydane:	IEEE 2025-01-01
Seria:	IEEE Access
Hasła przedmiotowe:	Anomalous sound detection supervised learning high-frequency feature Mel-Spectrogram Mel-frequency cepstral coefficients gated recurrent units
Dostęp online:	https://ieeexplore.ieee.org/document/10980326/
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