ASD-Diffusion: Anomalous Sound Detection with Diffusion Models
Unsupervised Anomalous Sound Detection (ASD) aims to design a generalizable method that can be used to detect anomalies when only normal sounds are given. In this paper, Anomalous Sound Detection based on Diffusion Models (ASD-Diffusion) is proposed for ASD in real-world factories. In our pipeline, the anomalies in acoustic features are reconstructed from their noisy corrupted features into their approximate normal pattern. Secondly, a post-processing anomalies filter algorithm is proposed to detect anomalies that exhibit significant deviation from the original input after reconstruction. Furthermore, denoising diffusion implicit model is introduced to accelerate the inference speed by a longer sampling interval of the denoising process. The proposed method is innovative in the application of diffusion models as a new scheme. Experimental results on the development set of DCASE 2023 challenge task 2 outperform the baseline by 7.75%, demonstrating the effectiveness of the proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingTask 2Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TLDiffGAN: A Latent Diffusion-GAN Framework with Temporal Information Fusion for Anomalous Sound Detection
Existing generative models for unsupervised anomalous sound detection are limited by their inability to fully capture the complex feature distribution of normal sounds, while the potential of powerful diffusion models in…
Statistical Test on Diffusion Model-based Anomaly Detection by Selective Inference
Advancements in AI image generation, particularly diffusion models, have progressed rapidly. However, the absence of an established framework for quantifying the reliability of AI-generated images hinders their use in cr…
Anomaly DetectionDecision MakingImage GenerationExtreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories
The study of the dynamics of natural and artificial systems has provided several examples of deviations from Brownian behavior, generally defined as anomalous diffusion. The investigation of these dynamics can provide a …
Feature EngineeringSynomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging
Ultrasound (US) imaging is widely used in routine clinical practice due to its advantages of being radiation-free, cost-effective, and portable. However, the low reproducibility and quality of US images, combined with th…
Anomaly DetectioncounterfactualUnsupervised Anomaly DetectionDistributed collaborative anomalous sound detection by embedding sharing
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn an anomalous sound detection model while …
Federated Learning