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Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

arXiv 2006.05822 ↗First posted 10 Jun 2020eess.AS cs.LG cs.SDcited by 43

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Abstract (arXiv)

In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to identify whether the sound emitted from a target machine is normal or anomalous. The main challenge of this task is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data. We have designed this challenge as the first benchmark of ASD research, which includes a large-scale dataset, evaluation metrics, and a simple baseline system. We received 117 submissions from 40 teams, and several novel approaches have been developed as a result of this challenge. On the basis of the analysis of the evaluation results, we discuss two new approaches and their problems.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2020.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 7; 2024: 8; 2025: 9; 2026: 7.

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  1. 2021MLPerf Tiny Benchmarkcited by 118
  2. 2022Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniquescited by 18
  3. 2023Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 18
  4. 2023Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approachcited by 17
  5. 2021Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditionscited by 16
  6. 2021Flow-based Self-supervised Density Estimation for Anomalous Sound Detectioncited by 15
  7. 2024MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AIcited by 15
  8. 2024Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 11
  9. 2021MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditionscited by 9
  10. 2023Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretrainingcited by 7
  11. 2023Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devicescited by 7
  12. 2024MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devicescited by 5
31 more
  1. 2023Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?cited by 4
  2. 2024Deep Generic Representations for Domain-Generalized Anomalous Sound Detectioncited by 4
  3. 2023Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detectioncited by 3
  4. 2024ASD-Diffusion: Anomalous Sound Detection with Diffusion Modelscited by 3
  5. 2022Robust Audio Anomaly Detectioncited by 2
  6. 2022Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detectioncited by 2
  7. 2022Anomalous Sound Detection Based on Machine Activity Detectioncited by 2
  8. 2022Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detectioncited by 2
  9. 2022Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasetscited by 2
  10. 2024Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbankscited by 2
  11. 2025AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Promptingcited by 2
  12. 2025FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representationcited by 2
  13. 2021Anomalous Sound Detection Using a Binary Classification Model and Class Centroidscited by 1
  14. 2024Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Descriptioncited by 1
  15. 2025From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithmscited by 1
  16. 2025Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classificationcited by 1
  17. 2025WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizationscited by 1
  18. 2026Toward Faithful Explanations in Acoustic Anomaly Detectioncited by 1
  19. 2021Environment Transfer for Distributed Systemscited by 0
  20. 2023Weakly Supervised Detection of Baby Crycited by 0
  21. 2024Distributed collaborative anomalous sound detection by embedding sharingcited by 0
  22. 2025MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detectioncited by 0
  23. 2025ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signalscited by 0
  24. 2025ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detectioncited by 0
  25. 2025Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detectioncited by 0
  26. 2026TLDiffGAN: A Latent Diffusion-GAN Framework with Temporal Information Fusion for Anomalous Sound Detectioncited by 0
  27. 2026Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detectioncited by 0
  28. 2026Spectrogram features for audio and speech analysiscited by 0
  29. 2026Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inferencecited by 0
  30. 2026MemNMF: Memory-Augmented NMF on LPC Spectra for Anomalous Sound Detectioncited by 0
  31. 2026Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Studycited by 0