Variational Component Decoder for Source Extraction from Nonlinear Mixture
In many practical scenarios of signal extraction from a nonlinear mixture, only one (signal) source is intended to be extracted. However, modern methods involving Blind Source Separation are inefficient for this task since they are designed to recover all sources in the mixture. In this paper, we propose supervised Variational Component Decoder (sVCD) as a method dedicated to extracting a single source from nonlinear mixture. sVCD leverages the sequence-to-sequence (Seq2Seq) translation ability of a specially designed neural network to approximate a nonlinear inverse of the mixture process, assisted by priors of the interested source. In order to maintain the robustness in the face of real-life samples, sVCD combines Seq2Seq with variational inference to form a deep generative model, and it is trained by optimizing a variant of variational bound on the data likelihood concerning only the interested source. We demonstrate that sVCD has superior performance on nonlinear source extraction over a state-of-the-art method on diverse datasets, including artificially generated sequences, radio frequency (RF) sensing data, and electroencephalogram (EEG) results.
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