# Deep Generic Representations for Domain-Generalized Anomalous Sound Detection

arXiv: https://arxiv.org/abs/2409.05035 · HTML: https://mlsanity.com/paper/2409.05035/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 8 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [Deep Nearest Neighbor Anomaly Detection](https://mlsanity.com/paper/2002.10445/) (2020)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection](https://mlsanity.com/paper/1908.03299/) (2019)
- [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)
- [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)
- [First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline](https://mlsanity.com/paper/2303.00455/) (2023)
- [Anomalous Sound Detection using Spectral-Temporal Information Fusion](https://mlsanity.com/paper/2201.05510/) (2022)
- [AnoPatch: Towards Better Consistency in Machine Anomalous Sound Detection](https://mlsanity.com/paper/2406.11364/) (2024)
- [Self-Supervised Learning for Anomalous Sound Detection](https://mlsanity.com/paper/2312.09578/) (2023)
- [Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection](https://mlsanity.com/paper/2305.12111/) (2023)
- [Robust Audio Anomaly Detection](https://mlsanity.com/paper/2202.01784/) (2022)

## Built on by (most cited first, 5 of 5)

- [Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?](https://mlsanity.com/paper/2609.26460/) (2026)
- [Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection](https://mlsanity.com/paper/2603.13749/) (2026)
- [Training-Free Model Selection and Domain-Aware Score Calibration for First-Shot Anomalous Sound Detection](https://mlsanity.com/paper/2607.04526/) (2026)
- [Anomaly-Free Self-Optimization via AUC Bounds](https://mlsanity.com/paper/2609.27362/) (2026)
- [Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate](https://mlsanity.com/paper/2610.19893/) (2026)

## Abstract (arXiv)

Developing a reliable anomalous sound detection (ASD) system requires robustness to noise, adaptation to domain shifts, and effective performance with limited training data. Current leading methods rely on extensive labeled data for each target machine type to train feature extractors using Outlier-Exposure (OE) techniques, yet their performance on the target domain remains sub-optimal. In this paper, we present textitGenRep, which utilizes generic feature representations from a robust, large-scale pre-trained feature extractor combined with kNN for domain-generalized ASD, without the need for fine-tuning. textitGenRep incorporates MemMixup, a simple approach for augmenting the target memory bank using nearest source samples, paired with a domain normalization technique to address the imbalance between source and target domains. textitGenRep outperforms the best OE-based approach without a need for labeled data with an Official Score of 73.79% on the DCASE2023T2 Eval set and demonstrates robustness under limited data scenarios. The code is available open-source.