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Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques [article]

Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi
2022 arXiv   pre-print
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: "Unsupervised anomalous sound detection (ASD) for machine condition monitoring  ...  In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts.  ...  To solve the problem described above, we designed DCASE challenge 2022 Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring Applying Domain Generalization Techniques".  ... 
arXiv:2206.05876v1 fatcat:bz4t7cf22fai3afrru3saoynwu

Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection [article]

Bingqing Chen, Luca Bondi, Samarjit Das
2022 arXiv   pre-print
We define multiple auxiliary classification tasks based on meta-information and leverage gradient-based meta-learning to improve generalization to different shifts.  ...  Grounded in the application of machine health monitoring, we propose a framework that adapts to new conditions with few-shot samples.  ...  INTRODUCTION Anomaly detection is the task of identifying anomalous observations [1] - [5] . In this paper, we focus on anomaly detection applied to machine health monitoring via audio signal.  ... 
arXiv:2204.01905v1 fatcat:ea4dke27mjfh3h2n75fzthisbm

A Large-Scale Benchmark Dataset for Anomaly Detection and Rare Event Classification for Audio Forensics

Ahmed Abbasi, Abdul Rehman Javed, Amanullah Yasin, Zunera Jalil, Natalia Kryvinska, Usman Tariq
2022 IEEE Access  
., beach, restaurant, and train) to focus on both detection of anomalous audio and classification of rare sound (e.g., events-baby cry, gunshots, broken glasses, footsteps) events for audio forensics.  ...  Several challenges for data analysis have emerged to extract useful information from multimedia data. One such challenge is the early and accurate detection of anomalies in multimedia data.  ...  They used the dataset named DCASE challenge Task 2 published in 2017 [33] .  ... 
doi:10.1109/access.2022.3166602 fatcat:2u6hcb7dvra45divf2prokn5ta

MARVEL - D3.1: Multimodal and privacy-aware audio-visual intelligence – initial version

Alexandros Iosifidis
2022 Zenodo  
These include methods for Sound Event De- tection, Sound Event Localisation and Detection, Automated Audio Captioning, Visual Anomaly Detection, Visual Crowd Counting, Audio-Visual Crowd Counting, as well  ...  as methodologies for improving the training and efficiency of AI models under supervised, unsupervised, and cross-modal contrastive learning settings.  ...  Koizumi for their input on previously reported results, and to acknowledge CSC-IT Center for Science, Finland, for computational resources.  ... 
doi:10.5281/zenodo.6821317 fatcat:eia7rkk5lfbg7khs3qcat5qd3m

Robust Audio Anomaly Detection

Wo Jae Lee, Karim Helwani, Arvindh Krishnaswamy, Srikanth Tenneti
We propose an outlier robust multivariate time series model which can be used for detecting previously unseen anomalous sounds based on noisy training data.  ...  We validate our solution using publicly available machine sound datasets. We demonstrate the effectiveness of our approach in anomaly detection by comparing against several state-of-the-art models.  ...  INTRODUCTION The task of anomaly detection has many natural applications and has been studied within diverse research areas and application domains such as monitoring financial indicators, machine health  ... 
doi:10.48550/arxiv.2202.01784 fatcat:bkkdljcq65evnkgvldy25hgdte