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As the labor force decreases, the demand for labor-saving automatic anomaloussound detection technology that conducts maintenance of industrial equipmenthas grown. Conventional approaches detect anomalies based on the reconstructionerrors of an autoencoder. However, when the target machine sound isnon-stationary, a reconstruction error tends to be large independent of ananomaly, and its variations increased because of the difficulty of predictingthe edge frames. To solve the issue, we propose an approach to anomalousdetection in which the model utilizes multiple frames of a spectrogram whosecenter frame is removed as an input, and it predicts an interpolation of theremoved frame as an output. Rather than predicting the edge frames, theproposed approach makes the reconstruction error consistent with the anomaly.Experimental results showed that the proposed approach achieved 27% improvementbased on the standard AUC score, especially against non-stationary machinerysounds.

Anomalous sound detection based on interpolation deep neural network