6 citations · 8 across the 4 of their papers we have counts for
4 papers
Consistency-aware multi-channel speech enhancement using deep neural networks
Yoshiki Masuyama, Masahito Togami, Tatsuya Komatsu
This paper proposes a deep neural network (DNN)-based multi-channel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal.…
Unsupervised Training for Deep Speech Source Separation with Kullback-Leibler Divergence Based Probabilistic Loss Function
Masahito Togami, Yoshiki Masuyama, Tatsuya Komatsu +1
In this paper, we propose a multi-channel speech source separation with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an al…
Multi-channel Time-Varying Covariance Matrix Model for Late Reverberation Reduction
Masahito Togami
In this paper, a multi-channel time-varying covariance matrix model for late reverberation reduction is proposed. Reflecting that variance of the late reverberation is time-varying…
Multichannel Loss Function for Supervised Speech Source Separation by Mask-based Beamforming
Yoshiki Masuyama, Masahito Togami, Tatsuya Komatsu
In this paper, we propose two mask-based beamforming methods using a deep neural network (DNN) trained by multichannel loss functions. Beamforming technique using time-frequency (T…