In this paper, we develop a modular deep convolutional autoencoder with a dense bottleneck structure to perform the task of unsupervised anomaly detection in machine operating sounds. The proposed model consists of multiple sub-networks with identical encoder-decoder structures, trained to learn a mapping function between different mel-scaled frequency bands. Experiments were conducted on the recently introduced MIMII (Malfunctioning Industrial Machine Inspection and Investigation) open benchmark dataset. Experimental results demonstrate that the proposed model yields improved fault detection performance in terms of the Area Under Curve (AUC) metric compared to the baseline approach.
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