143 citations · 527 across the 38 of their papers we have counts for
4 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…
Trainable Adaptive Window Switching for Speech Enhancement
Yuma Koizumi, Noboru Harada, Yoichi Haneda
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform…
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…