  The task of blind source separation (BSS) involves separating sources from a
mixture without prior knowledge of the sources or the mixing system.
Single-channel mixtures and non-linear mixtures are a particularly challenging
problem in BSS. In this paper, we propose a novel method for addressing BSS
with single-channel non-linear mixtures by leveraging the natural feature
subspace specialization ability of multi-encoder autoencoders. During the
training phase, our method unmixes the input into the separate encoding spaces
of the multi-encoder network and then remixes these representations within the
decoder for a reconstruction of the input. Then to perform source inference, we
introduce a novel encoding masking technique whereby masking out all but one of
the encodings enables the decoder to estimate a source signal. To this end, we
also introduce a sparse mixing loss that encourages sparse remixing of source
encodings throughout the decoder and a so-called zero reconstruction loss on
the decoder for coherent source estimations. To analyze and evaluate our
method, we conduct experiments on a toy dataset, designed to demonstrate this
property of feature subspace specialization, and with real-world biosignal
recordings from a polysomnography sleep study for extracting respiration from
electrocardiogram and photoplethysmography signals.
