ANOMALOUS SOUND DETECTION BASED ON ATTENTION MECHANISM

Hayato Mori, Satoshi Tamura, Satoru Hayamizu

研究成果: Conference contribution

抄録

For the automation of maintenance of mechanical facilities and devices, anomalous sound detection from machines has been explored. For these years, methods by machine learning and deep learning have been proposed for anomaly detection in various fields. Some deep-learning-based works calculate an anomaly score based on reconstruction errors obtained from an autoencoder model. However, the performance may not be sufficient, depending on characteristics of machines. In this study, we propose a method for detecting anomalous sounds using an autoencoder model with an attention-based mechanism. Given multiple frames of the log-scale mel spectrogram with a missing frame, our model computes the reconstruction error between an predicted frame and the removed frame as an abnormal score. We conducted experiments to compare our scheme to conventional ones, with visualizing attention weights. Our method achieved better performance, and it is found the missing frame can be well predicted using surrounds frames emphasized by the attention model. It is also found our approach can perform well independent on kind of machines and the number of input frames.

本文言語English
ホスト出版物のタイトル29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ581-585
ページ数5
ISBN(電子版)9789082797060
DOI
出版ステータスPublished - 2021
外部発表はい
イベント29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
継続期間: 2021 8月 232021 8月 27

出版物シリーズ

名前European Signal Processing Conference
2021-August
ISSN(印刷版)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
国/地域Ireland
CityDublin
Period21/8/2321/8/27

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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