# Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

arXiv: https://arxiv.org/abs/2305.07828 · HTML: https://mlsanity.com/paper/2305.07828/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 11 of 11)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [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)
- [MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task](https://mlsanity.com/paper/2205.13879/) (2022)
- [Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma](https://mlsanity.com/paper/1810.09133/) (2018)
- [Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions](https://mlsanity.com/paper/2106.04492/) (2021)
- [First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline](https://mlsanity.com/paper/2303.00455/) (2023)
- [Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization](https://mlsanity.com/paper/2009.12042/) (2020)
- [Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds](https://mlsanity.com/paper/1907.08338/) (2019)

## Built on by (most cited first, 18 of 18)

- [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)
- [Self-Supervised Learning for Anomalous Sound Detection](https://mlsanity.com/paper/2312.09578/) (2023)
- [Deep Generic Representations for Domain-Generalized Anomalous Sound Detection](https://mlsanity.com/paper/2409.05035/) (2024)
- [Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?](https://mlsanity.com/paper/2309.15643/) (2023)
- [Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models](https://mlsanity.com/paper/2409.07016/) (2024)
- [ASD-Diffusion: Anomalous Sound Detection with Diffusion Models](https://mlsanity.com/paper/2409.15957/) (2024)
- [FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation](https://mlsanity.com/paper/2507.16696/) (2025)
- [CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concerns](https://mlsanity.com/paper/2408.14753/) (2024)
- [MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System](https://mlsanity.com/paper/2409.18542/) (2024)
- [False Sense of Safety in Selective Signal Classification: Auditing Bound Tightness and Exchangeability for Risk Control](https://mlsanity.com/paper/2606.15153/) (2026)
- [Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?](https://mlsanity.com/paper/2609.26460/) (2026)
- [Revisiting SSL for sound event detection: complementary fusion and adaptive post-processing](https://mlsanity.com/paper/2505.11889/) (2025)
- [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)
- [A Framework for Evaluating Faithfulness in Explainable AI for Machine Anomalous Sound Detection Using Frequency-Band Perturbation](https://mlsanity.com/paper/2601.19017/) (2026)
- [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)
- [Anomaly-Free Self-Optimization via AUC Bounds](https://mlsanity.com/paper/2609.27362/) (2026)

## Abstract (arXiv)

We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: ``First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring''. The main goal is to enable rapid deployment of ASD systems for new kinds of machines without the need for hyperparameter tuning. In the past ASD tasks, developed methods tuned hyperparameters for each machine type, as the development and evaluation datasets had the same machine types. However, collecting normal and anomalous data as the development dataset can be infeasible in practice. In 2023 Task 2, we focus on solving the first-shot problem, which is the challenge of training a model on a completely novel machine type. Specifically, (i) each machine type has only one section (a subset of machine type) and (ii) machine types in the development and evaluation datasets are completely different. Analysis of 86 submissions from 23 teams revealed that the keys to outperform baselines were: 1) sampling techniques for dealing with class imbalances across different domains and attributes, 2) generation of synthetic samples for robust detection, and 3) use of multiple large pre-trained models to extract meaningful embeddings for the anomaly detector.