143 citations · 527 across the 38 of their papers we have counts for
3 papers · 1 filter
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation
Masataka Yamaguchi, Yuma Koizumi, Noboru Harada
We tackle unsupervised anomaly detection (UAD), a problem of detecting data that significantly differ from normal data. UAD is typically solved by using density estimation. Recentl…
DNN-based Source Enhancement to Increase Objective Sound Quality Assessment Score
Yuma Koizumi, Kenta Niwa, Yusuke Hioka +2
We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation o…
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsum Yuta Kawachi +1
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS…