Abstract
Anomalous Sound Detection by Deep Learning could be used to automatically detect malfunctions in mechanical products from their operating sounds. With many normal sounds but very few anomalous sounds available for training, one-class classification methods applied to sound spectrograms are the most suitable methods. Among them, the Masked Autoencoder for Distribution Estimation (MADE) method showed state-of-the-art results with transient anomalous sounds but poor results with sustained anomalous sounds. The loss of the spectro-temporal structure in the inputs possibly explaining this issue, we have modified a method recently designed for 2D image completion, taking advantage of the full spectro-temporal structure of the inputs. When evaluated on industrial data, this new method outperforms a baseline Autoencoder method and the original MADE method.
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