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Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds
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Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds

Alexandrine Ribeiro
Luís Miguel Matos
P. Pereira
E. C. Nunes
André L. Ferreira
P. Cortez
A. Pilastri
ArXiv (abs)PDFHTML

Papers citing "Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds"

3 / 3 papers shown
Title
Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection
Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection
Sahan Dissanayaka
Manjusri Wickramasinghe
Pasindu Marasinghe
33
0
0
25 Oct 2024
Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech
Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech
Szu-Wei Fu
Kuo-Hsuan Hung
Yu Tsao
Yu-Chiang Frank Wang
SSL
78
13
0
26 Feb 2024
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection
Saad Abbasi
M. Famouri
M. Shafiee
A. Wong
61
26
0
31 Mar 2021