# Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training

arXiv: https://arxiv.org/abs/2509.12845 · HTML: https://mlsanity.com/paper/2509.12845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [EAT: Self-Supervised Pre-Training with Efficient Audio Transformer](https://mlsanity.com/paper/2401.03497/) (2024)
- [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)
- [AnoPatch: Towards Better Consistency in Machine Anomalous Sound Detection](https://mlsanity.com/paper/2406.11364/) (2024)
- [Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2506.10097/) (2025)
- [Self-Supervised Learning for Anomalous Sound Detection](https://mlsanity.com/paper/2312.09578/) (2023)
- [Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?](https://mlsanity.com/paper/2309.15643/) (2023)
- [A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection](https://mlsanity.com/paper/2409.03610/) (2024)
- [Improvements of Discriminative Feature Space Training for Anomalous Sound Detection in Unlabeled Conditions](https://mlsanity.com/paper/2409.09332/) (2024)
- [An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models](https://mlsanity.com/paper/2508.15334/) (2025)

## Abstract (arXiv)

Anomalous Sound Detection (ASD) is often formulated as a machine attribute classification task, a strategy necessitated by the common scenario where only normal data is available for training. However, the exhaustive collection of machine attribute labels is laborious and impractical. To address the challenge of missing attribute labels, this paper proposes an agglomerative hierarchical clustering method for the assignment of pseudo-attribute labels using representations derived from a domain-adaptive pre-trained model, which are expected to capture machine attribute characteristics. We then apply model adaptation to this pre-trained model through supervised fine-tuning for machine attribute classification, resulting in a new state-of-the-art performance. Evaluation on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge dataset demonstrates that our proposed approach yields significant performance gains, ultimately outperforming our previous top-ranking system in the challenge.