63 citations · 364 across the 60 of their papers we have counts for
3 papers · 1 filter
Closing the Training/Inference Gap for Deep Attractor Networks
Cyril Cadoux, Stefan Uhlich, Marc Ferras +1
This paper improves the deep attractor network (DANet) approach by closing its gap between training and inference. During training, DANet relies on attractors, which are computed f…
Improving Voice Separation by Incorporating End-to-end Speech Recognition
Naoya Takahashi, Mayank Kumar Singh, Sakya Basak +3
Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data. In this work, we propos…
Recursive speech separation for unknown number of speakers
Naoya Takahashi, Sudarsanam Parthasaarathy, Nabarun Goswami +1
In this paper we propose a method of single-channel speaker-independent multi-speaker speech separation for an unknown number of speakers. As opposed to previous works, in which th…