🌟 Awesome Anomalous Sound Detection (ASD) 🌟

A comprehensive and meticulously curated list of resources, datasets, and cutting-edge papers for Anomalous Sound Detection.

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🔖 News!!!

🚀 Stay tuned! We are continuously updating this repository with the latest breakthroughs in audio anomaly detection.

📌 We are actively tracking the latest research and welcome contributions to our repository and survey paper. If your studies are relevant, please feel free to contact us.

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🎁 How to contribute to this repository?

Since the following content is generated based on our database, please provide the following information in the issue to help us fill in the database to add new papers (please do not submit a PR directly).

1. Paper title
2. arXiv ID (if any)
3. Publication status (if any)

Review and Count

Pub	Year	Datasets	Note
ICASSP	2020-2025	DCASE2020 /DCASE2022
ICANN	2025	DCASE2024
TSALP	2023、2025	DCSASE
SPL	2025	DCSASE
Applied Acoustics	2023	DCASE
ArXiv	ArXiv	DCASE	...
DCASE	2020-2024	DCASE
EUSIPCO	2018、2021	Sound Ideas Series 6000 General Sound Effects Library 、DCASE
ICCE	2020	MIMII
Digital Signal Processing	2023	DCASE

🗂️ Table of Contents

1. 📝 Papers
   • Dual Models
   • Generative Models
2. 🔗 Other Resources
3. ✍️ Contributing

🗂️ dataset

DCASE20

Download the dataset from Audio dataset.

📝 Papers

ICASSP

Dual Models

1. SW-WAVENET: Learning Representation from Spectrogram and Wavegram Using Wavenet for Anomalous Sound Detection.

   

   H. Chen, L. Ran, X. Sun and C. Cai. ICASSP'24. 🔥

2. NOISY-ARCMIX: ADDITIVE NOISY ANGULAR MARGIN LOSS COMBINED WITH MIXUP FOR ANOMALOUS SOUND DETECTION. CODE.

   

   Soonhyeon Choi, Jung-Woo Choi. ICASSP'24. 🔥

3. A DUAL-PATH FRAMEWORK WITH FREQUENCY-AND-TIME EXCITED NETWORK FOR ANOMALOUS SOUND DETECTION.

   

   Yucong Zhang, Juan Liu, Yao Tian, Haifeng Liu, Ming Li. ICASSP'24. 🔥

4. Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection under Domain Shift.

   

   H. Lan, Q. Zhu, J. Guan, Y. Wei and W. Wang. ICASSP'24. 🔥

5. DP-MAE: A Dual-Path Masked Autoencoder Based Self-Supervised Learning Method for Anomalous Sound Detection.

   

   Z. -L. Liu, Y. Song, X. -M. Zeng, L. -R. Dai and I. McLoughlin. ICASSP'24. 🔥

6. Anomalous Sound Detection Using Audio Representation with Machine ID Based Contrastive Learning Pretraining.

   

   J. Guan, F. Xiao, Y. Liu, Q. Zhu and W. Wang. ICASSP'23. 🔥

7. Anomalous Sound Detection Using Spectral-Temporal Information Fusion. code

   

   Youde Liu; Jian Guan; Qiaoxi Zhu; Wenwu Wang. ICASSP'22. 🔥

Generative Models

1. UNSUPERVISED ANOMALY DETECTION AND LOCALIZATION OF MACHINE AUDIO: A GAN-BASED APPROACH.

2. [CODE]

   

   A. Jiang, W. -Q. Zhang, Y. Deng, P. Fan and J. Liu. ICASSP'23. 🔥

GMM Models

1. Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection.

   

   A. Jiang, W. -Q. Zhang, Y. Deng, P. Fan and J. Liu. ICASSP'23. 🔥

Other Models

1. Improvements of Discriminative Feature Space Training for Anomalous Sound Detection in Unlabeled Conditions.

   Takuya Fujimura et al. ICASSP'25. 🔥

2. An Effective Anomalous Sound Detection Method Based on Representation Learning with Simulated Anomalies.

   

   H. Chen et al. ICASSP'23. 🔥

3. Self-Supervised Representation Learning for Unsupervised Anomalous Sound Detection Under Domain Shift.

   

   H. Chen, Y. Song, L. -R. Dai, I. McLoughlin and L. Liu. ICASSP'23. 🔥

4. Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection.

   

   S. Huang, Z. Fang, L. and L. He. ICASSP'25. 🔥

5. An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models.

   Guirui Zhong, Qing Wang. and Xin Fang. ICASSP'25. 🔥

SPL

1. ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection

   

C. Ma, P. Jia and W. Yang. SPL'25. 🔥

TASLP

1. Why Do Angular Margin Losses Work Well for Semi-Supervised Anomalous Sound Detection?

   

   HK. Wilkinghoff and F. Kurth. IEEE/ACM Transactions on Audio, Speech, and Language Processing 2023. 🔥

2. Why Do Angular Margin Losses Work Well for Semi-Supervised Anomalous Sound Detection?

   

   HK. Wilkinghoff and F. Kurth. IEEE/ACM Transactions on Audio, Speech, and Language Processing 2023. 🔥

3. AdaProj: Adaptively Scaled Angular Margin Subspace Projections for Anomalous Sound Detection with Auxiliary Classification Tasks

   

   Kevin Wilkinghoff. DCASE 2024. 🔥

Applied Acoustics

1. Unsupervised anomalous sound detection for industrial monitoring based on ArcFace classifier and gaussian mixture model

Ji Wu ,Fei Yang . Applied Acoustics Volume 203, 28 February 2023, 109188. 🔥

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