# Transformer-based Autoencoder with ID Constraint for Unsupervised Anomalous Sound Detection

arXiv: https://arxiv.org/abs/2310.08950 · HTML: https://mlsanity.com/paper/2310.08950/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [ASD-Diffusion: Anomalous Sound Detection with Diffusion Models](https://mlsanity.com/paper/2409.15957/) (2024)
- [Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbanks](https://mlsanity.com/paper/2409.05319/) (2024)
- [An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models](https://mlsanity.com/paper/2508.15334/) (2025)

## Abstract (arXiv)

Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. The self-supervised methods are not always stable and perform differently, even for machines of the same type. In addition, the anomalous sound may be short-lived, making it even harder to distinguish from normal sound. This paper proposes an ID constrained Transformer-based autoencoder (IDC-TransAE) architecture with weighted anomaly score computation for unsupervised ASD. Machine ID is employed to constrain the latent space of the Transformer-based autoencoder (TransAE) by introducing a simple ID classifier to learn the difference in the distribution for the same machine type and enhance the ability of the model in distinguishing anomalous sound. Moreover, weighted anomaly score computation is introduced to highlight the anomaly scores of anomalous events that only appear for a short time. Experiments performed on DCASE 2020 Challenge Task2 development dataset demonstrate the effectiveness and superiority of our proposed method.