6 citations · 12 across the 9 of their papers we have counts for
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eess.AS2020
Surrogate Source Model Learning for Determined Source Separation
Robin Scheibler, Masahito Togami
We propose to learn surrogate functions of universal speech priors for determined blind speech separation. Deep speech priors are highly desirable due to their high modelling power…
eess.AS2020★ 1 cited
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.…