Abnormal sound detection based on composite autoencoder Gaussian mixture model

Heng Wang; Jie Liu; Shuaifeng Li

doi:10.1117/12.2682257

26 May 2023 Abnormal sound detection based on composite autoencoder Gaussian mixture model

Heng Wang, Jie Liu, Shuaifeng Li

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127001W (2023) https://doi.org/10.1117/12.2682257
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Aiming at the problem that the accuracy of abnormal sound detection under unsupervised conditions is not ideal, a novel abnormal sound detection model using composite self-coder combined with Gaussian mixture model is proposed. Firstly, the timing structure and gating mechanism of LSTM are used to improve the feature extraction ability of self-coder (including self-coder and variational self-coder), Secondly, Gaussian Mixture Model (GMM) is used to generate artificial data to improve the robustness of the self-coder against background noise. Experiments are carried out using ToyADMOS and MIMII public data sets, and the results are superior to the naive self-coder and the two improved self-coding models. On the six machines of the experimental data set, AUC increases by 6.34%, 6.65%, 4.03%, 5.57%, 2.38% and 1.07% respectively.

Citation Download Citation

Heng Wang, Jie Liu, and Shuaifeng Li "Abnormal sound detection based on composite autoencoder Gaussian mixture model", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127001W (26 May 2023); https://doi.org/10.1117/12.2682257

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