ANOMALOUS SOUND DETECTION BASED ON ATTENTION MECHANISM

Hayato Mori, Satoshi Tamura, Satoru Hayamizu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages581-585
Number of pages5
ISBN (Electronic)9789082797060
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 2021 Aug 232021 Aug 27

Publication series

NameEuropean Signal Processing Conference
Volume2021-August
ISSN (Print)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period21/8/2321/8/27

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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