Skip-connection and transformer-enhanced convolutional autoencoders detect expert-labeled anomalies in real wood planer audio with AUC up to 0.875, on a newly released factory dataset.
Multisensor data fusion and machine learning to classify wood products and predict workpiece characteristics during milling,
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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers
Skip-connection and transformer-enhanced convolutional autoencoders detect expert-labeled anomalies in real wood planer audio with AUC up to 0.875, on a newly released factory dataset.