# Anomalous Sound Detection using Spectral-Temporal Information Fusion

arXiv: https://arxiv.org/abs/2201.05510 · HTML: https://mlsanity.com/paper/2201.05510/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2022.

## Built on by (most cited first, 12 of 12)

- [Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining](https://mlsanity.com/paper/2304.03588/) (2023)
- [Deep Generic Representations for Domain-Generalized Anomalous Sound Detection](https://mlsanity.com/paper/2409.05035/) (2024)
- [Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detection](https://mlsanity.com/paper/2310.06364/) (2023)
- [CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concerns](https://mlsanity.com/paper/2408.14753/) (2024)
- [Spectral-Temporal Fusion Representation for Person-in-Bed Detection](https://mlsanity.com/paper/2412.19404/) (2024)
- [ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection](https://mlsanity.com/paper/2509.02471/) (2025)
- [Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection](https://mlsanity.com/paper/2509.13853/) (2025)
- [Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detection](https://mlsanity.com/paper/2509.15570/) (2025)
- [TLDiffGAN: A Latent Diffusion-GAN Framework with Temporal Information Fusion for Anomalous Sound Detection](https://mlsanity.com/paper/2602.01060/) (2026)
- [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)

Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of machines from normal sounds. However, the state-of-the-art approaches are not always stable and perform dramatically differently even for machines of the same type, making it impractical for general applications. This paper proposes a spectral-temporal fusion based self-supervised method to model the feature of the normal sound, which improves the stability and performance consistency in detection of anomalous sounds from individual machines, even of the same type. Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed method achieved 81.39%, 83.48%, 98.22% and 98.83% in terms of the minimum AUC (worst-case detection performance amongst individuals) in four types of real machines (fan, pump, slider and valve), respectively, giving 31.79%, 17.78%, 10.42% and 21.13% improvement compared to the state-of-the-art method, i.e., GlowAff. Moreover, the proposed method has improved AUC (average performance of individuals) for all the types of machines in the dataset. The source codes are available at https://github.com/liuyoude/STgramMFN