21 citations · 33 across the 10 of their papers we have counts for
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eess.AS2019★ 1 cited
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…
eess.AS2019★ 6 cited
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…
cs.SD2019
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…