3 citations · 6 across the 3 of their papers we have counts for
3 papers
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds
Yuma Koizumi, Shoichiro Saito, Masataka Yamaguchi +2
Use of an autoencoder (AE) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds (ADS). The AE is trained to minimize the sample mean of th…
Data-driven design of perfect reconstruction filterbank for DNN-based sound source enhancement
Daiki Takeuchi, Kohei Yatabe, Yuma Koizumi +2
We propose a data-driven design method of perfect-reconstruction filterbank (PRFB) for sound-source enhancement (SSE) based on deep neural network (DNN). DNNs have been used to est…
Deep Griffin-Lim Iteration
Yoshiki Masuyama, Kohei Yatabe, Yuma Koizumi +2
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN). T…