# AnoPatch: Towards Better Consistency in Machine Anomalous Sound Detection

arXiv: https://arxiv.org/abs/2406.11364 · HTML: https://mlsanity.com/paper/2406.11364/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2024.

## Built on by (most cited first, 11 of 11)

- [Deep Generic Representations for Domain-Generalized Anomalous Sound Detection](https://mlsanity.com/paper/2409.05035/) (2024)
- [Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models](https://mlsanity.com/paper/2409.07016/) (2024)
- [FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation](https://mlsanity.com/paper/2507.16696/) (2025)
- [An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models](https://mlsanity.com/paper/2508.15334/) (2025)
- [Multimodal Laryngoscopic Video Analysis for Assisted Diagnosis of Vocal Fold Paralysis](https://mlsanity.com/paper/2409.03597/) (2024)
- [ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals](https://mlsanity.com/paper/2508.14689/) (2025)
- [Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training](https://mlsanity.com/paper/2509.12845/) (2025)
- [Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection](https://mlsanity.com/paper/2509.13853/) (2025)
- [Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection](https://mlsanity.com/paper/2603.13749/) (2026)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)
- [UD-ASD: A Unified Diffusion Model for Anomalous Sound Detection](https://mlsanity.com/paper/2607.12576/) (2026)

## Abstract (arXiv)

Large pre-trained models have demonstrated dominant performances in multiple areas, where the consistency between pre-training and fine-tuning is the key to success. However, few works reported satisfactory results of pre-trained models for the machine anomalous sound detection (ASD) task. This may be caused by the inconsistency of the pre-trained model and the inductive bias of machine audio, resulting in inconsistency in data and architecture. Thus, we propose AnoPatch which utilizes a ViT backbone pre-trained on AudioSet and fine-tunes it on machine audio. It is believed that machine audio is more related to audio datasets than speech datasets, and modeling it from patch level suits the sparsity of machine audio. As a result, AnoPatch showcases state-of-the-art (SOTA) performances on the DCASE 2020 ASD dataset and the DCASE 2023 ASD dataset. We also compare multiple pre-trained models and empirically demonstrate that better consistency yields considerable improvement.