# Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions arXiv: https://arxiv.org/abs/2106.04492 ยท HTML: https://mlsanity.com/paper/2106.04492/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 12 of its 33 references that are in the map. - First posted on arXiv 8 Jun 2021. ## Builds on (most cited first, 12 of 12) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018) - [Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2006.05822/) (2020) - [Anomalous sound detection based on interpolation deep neural network](https://mlsanity.com/paper/2005.09234/) (2020) - [ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions](https://mlsanity.com/paper/2106.02369/) (2021) - [Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma](https://mlsanity.com/paper/1810.09133/) (2018) - [Flow-based Self-supervised Density Estimation for Anomalous Sound Detection](https://mlsanity.com/paper/2103.08801/) (2021) - [Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization](https://mlsanity.com/paper/2009.12042/) (2020) - [Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples](https://mlsanity.com/paper/2011.02949/) (2020) - [MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions](https://mlsanity.com/paper/2105.02702/) (2021) - [Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds](https://mlsanity.com/paper/1907.08338/) (2019) - [ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2007.05314/) (2020) ## Built on by (most cited first, 16 of 16) - [Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques](https://mlsanity.com/paper/2206.05876/) (2022) - [Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2305.07828/) (2023) - [Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2406.07250/) (2024) - [Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets](https://mlsanity.com/paper/2207.13430/) (2022) - [Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?](https://mlsanity.com/paper/2309.15643/) (2023) - [Multimedia Datasets for Anomaly Detection: A Review](https://mlsanity.com/paper/2112.05410/) (2021) - [Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection](https://mlsanity.com/paper/2204.01905/) (2022) - [Anomalous Sound Detection Based on Machine Activity Detection](https://mlsanity.com/paper/2204.07353/) (2022) - [FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation](https://mlsanity.com/paper/2507.16696/) (2025) - [MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System](https://mlsanity.com/paper/2409.18542/) (2024) - [Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description](https://mlsanity.com/paper/2412.10792/) (2024) - [Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sorensen-Dice Coefficient Loss and Transfer Learning](https://mlsanity.com/paper/2107.10471/) (2021) - [MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detection](https://mlsanity.com/paper/2507.20666/) (2025) - [ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals](https://mlsanity.com/paper/2508.14689/) (2025) - [Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection](https://mlsanity.com/paper/2603.13749/) (2026) - [Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference](https://mlsanity.com/paper/2604.13252/) (2026) ## Abstract (arXiv) We present the task description and discussion on the results of the DCASE 2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound detection (ASD) task, identifying whether a given sound was normal or anomalous without anomalous training data. In 2021, we organized an advanced unsupervised ASD task under domain-shift conditions, which focuses on the inevitable problem of the practical use of ASD systems. The main challenge of this task is to detect unknown anomalous sounds where the acoustic characteristics of the training and testing samples are different, i.e., domain-shifted. This problem frequently occurs due to changes in seasons, manufactured products, and/or environmental noise. We received 75 submissions from 26 teams, and several novel approaches have been developed in this challenge. On the basis of the analysis of the evaluation results, we found that there are two types of remarkable approaches that TOP-5 winning teams adopted: 1) ensemble approaches of ``outlier exposure'' (OE)-based detectors and ``inlier modeling'' (IM)-based detectors and 2) approaches based on IM-based detection for features learned in a machine-identification task.