  Consider a multichannel Ambisonic recording containing a mixture of several
reverberant speech signals. Retreiving the reverberant Ambisonic signals
corresponding to the individual speech sources blindly from the mixture is a
challenging task as it requires to estimate multiple signal channels for each
source. In this work, we propose AmbiSep, a deep neural network-based
plane-wave domain masking approach to solve this task. The masking network uses
learned feature representations and transformers in a triple-path processing
configuration. We train and evaluate the proposed network architecture on a
spatialized WSJ0-2mix dataset, and show that the method achieves a multichannel
scale-invariant signal-to-distortion ratio improvement of 17.7 dB on the blind
test set, while preserving the spatial characteristics of the separated sounds.
