mlsanityData to 7 Oct 2026

Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approach

arXiv 2303.17949 ↗First posted 31 Mar 2023cs.SD cs.LG eess.AScited by 9

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Abstract (arXiv)

Automatic detection of machine anomaly remains challenging for machine learning. We believe the capability of generative adversarial network (GAN) suits the need of machine audio anomaly detection, yet rarely has this been investigated by previous work. In this paper, we propose AEGAN-AD, a totally unsupervised approach in which the generator (also an autoencoder) is trained to reconstruct input spectrograms. It is pointed out that the denoising nature of reconstruction deprecates its capacity. Thus, the discriminator is redesigned to aid the generator during both training stage and detection stage. The performance of AEGAN-AD on the dataset of DCASE 2022 Challenge TASK 2 demonstrates the state-of-the-art result on five machine types. A novel anomaly localization method is also investigated. Source code available at: www.github.com/jianganbai/AEGAN-AD

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 31 Mar 2023.

Builds on

12 of 20 references.

References · 12

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  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2022Generative Adversarial Networkscited by 5,603
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  7. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  8. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  9. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  10. 2021ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditionscited by 26
  11. 2022MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Taskcited by 23
  12. 2022Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniquescited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 2.

9 papers.

  1. 2024Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Modelscited by 3
  2. 2024ASD-Diffusion: Anomalous Sound Detection with Diffusion Modelscited by 3
  3. 2025FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representationcited by 2
  4. 2024CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concernscited by 1
  5. 2025An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Modelscited by 1
  6. 2025Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detectioncited by 0
  7. 2025Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detectioncited by 0
  8. 2026TLDiffGAN: A Latent Diffusion-GAN Framework with Temporal Information Fusion for Anomalous Sound Detectioncited by 0
  9. 2026UD-ASD: A Unified Diffusion Model for Anomalous Sound Detectioncited by 0