5 citations · 20 across the 10 of their papers we have counts for
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cs.SD2019
Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model
Yoshiaki Bando, Yoko Sasaki, Kazuyoshi Yoshii
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals. Conventional neural separation methods require a lot of…
cs.CV2019★ 2 cited
Automatic Labeled LiDAR Data Generation based on Precise Human Model
Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi +1
Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of…