In this paper, we propose an anomaly detection algorithm for machine sounds with a deep complex network trained by self-supervision. Using the fact that phase continuity information is crucial for detecting abnormalities in time-series signals, our proposed algorithm utilizes the complex spectrum as an input and performs complex number arithmetic throughout the entire process. Since the usefulness of phase information can vary depending on the type of machine sound, we also apply an attention mechanism to control the weights of the complex and magnitude spectrum bottleneck features depending on the machine type. We train our network to perform a self-supervised task that classifies the machine identifier (id) of normal input sounds among multiple classes. At test time, an input signal is detected as anomalous if the trained model is unable to correctly classify the id. In other words, we determine the presence of an anomality when the output cross-entropy score of the multi-class identification task is lower than a pre-defined threshold. Experiments with the MIMII dataset show that the proposed algorithm has a much higher area under the curve (AUC) score than conventional magnitude spectrum-based algorithms.
|Title of host publication||29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings|
|Publisher||European Signal Processing Conference, EUSIPCO|
|Number of pages||5|
|Publication status||Published - 2021|
|Event||29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland|
Duration: 2021 Aug 23 → 2021 Aug 27
|Name||European Signal Processing Conference|
|Conference||29th European Signal Processing Conference, EUSIPCO 2021|
|Period||21/8/23 → 21/8/27|
Bibliographical noteFunding Information:
VI. ACKNOWLEDGEMENTS This work was partly supported by the Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MIST) (No. 2020-0-01361) and LG Display Co.
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering