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Real Time Detection of Acoustic Anomalies in Industrial Processes Using Sequential Autoencoders by Barış Bayram; Taha Berkay Duman; Gökhan Ince is a Computer Science article available to read on EtoBox.

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## Abstract Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next‐generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence‐to‐sequence Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single‐channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of Convolutional Long Short‐Term Memory AE (Conv‐LSTMAE) is evaluated and compared with sequential Convolutional AE

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Author

Barış Bayram; Taha Berkay Duman; Gökhan Ince

Publisher

John Wiley and Sons; Wiley (Blackwell Publishing); Blackwell Publishing Inc.; Wiley (ISSN 0266-4720)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)