activity
20182021
most citedBlind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation

27 citations · 28 across the 3 of their papers we have counts for

collaborators

7 papers

cs.LG2021

Acceleration Method for Learning Fine-Layered Optical Neural Networks

Kazuo Aoyama, Hiroshi Sawada

An optical neural network (ONN) is a promising system due to its high-speed and low-power operation. Its linear unit performs a multiplication of an input vector and a weight matri…

eess.AS202127 cited

Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation

Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita +2

This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we…

cs.SD20211 cited

A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter

Nobutaka Ito, Rintaro Ikeshita, Hiroshi Sawada +1

This paper presents a computationally efficient approach to blind source separation (BSS) of audio signals, applicable even when there are more sources than microphones (i.e., the…

cond-mat.mtrl-sci2020

Quantum transport evidence of Weyl fermions in an epitaxial ferromagnetic oxide

Kosuke Takiguchi, Yuki K. Wakabayashi, Hiroshi Irie +8

Magnetic Weyl fermions, which occur in magnets, have novel transport phenomena related to pairs of Weyl nodes, and they are, of both, scientific and technological interest, with th…

cond-mat.mtrl-sci2019

Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films

Yuki K. Wakabayashi, Takuma Otsuka, Yoshiharu Krockenberger +3

Materials informatics exploiting machine learning techniques, e.g., Bayesian optimization (BO), has the potential to offer high-throughput optimization of thin-film growth conditio…

physics.soc-ph2018

Finding Appropriate Traffic Regulations via Graph Convolutional Networks

Tomoharu Iwata, Takuma Otsuka, Hitoshi Shimizu +3

Appropriate traffic regulations, e.g. planned road closure, are important in congested events. Crowd simulators have been used to find appropriate regulations by simulating multipl…