mlsanityData to 14 Oct 2026

Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

arXiv 2206.05876 ↗First posted 13 Jun 2022cs.SD cs.LG eess.AScited by 18

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Abstract (arXiv)

We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''. Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. Analysis of 81 submissions from 31 teams revealed two remarkable types of domain generalization techniques: 1) domain-mixing-based approach that obtains generalized representations and 2) domain-classification-based approach that explicitly or implicitly classifies different domains to improve detection performance for each domain.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 13 Jun 2022.

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11 of 26 references.

References · 11

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2020Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 43
  4. 2020Anomalous sound detection based on interpolation deep neural networkcited by 34
  5. 2021ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditionscited by 26
  6. 2022MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Taskcited by 23
  7. 2018Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemmacited by 19
  8. 2021Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditionscited by 16
  9. 2020Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimizationcited by 12
  10. 2020Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examplescited by 10
  11. 2019Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Soundscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 3; 2025: 6; 2026: 4.

18 papers.

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  1. 2023Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approachcited by 9
  2. 2023Self-Supervised Learning for Anomalous Sound Detectioncited by 8
  3. 2023Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretrainingcited by 7
  4. 2023Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?cited by 4
  5. 2025A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challengescited by 4
  6. 2023Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detectioncited by 3
  7. 2024Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbankscited by 2
  8. 2025FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representationcited by 2
  9. 2025WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizationscited by 2
  10. 2024Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Descriptioncited by 1
  11. 2026Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?cited by 1
  12. 2024Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detectioncited by 0
6 more
  1. 2025Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domainscited by 0
  2. 2025MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detectioncited by 0
  3. 2025ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signalscited by 0
  4. 2026Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detectioncited by 0
  5. 2026Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inferencecited by 0
  6. 2026Anomaly-Free Self-Optimization via AUC Boundscited by 0