🤖 AI Summary
Addressing the long-standing challenge of single-channel nonlinear blind source separation (BSS), this paper proposes a novel multi-encoder autoencoder framework—the first to introduce a multi-encoder architecture for this task. The method centers on three key components: (i) an encoding mask mechanism that enforces specialization of latent representations into distinct source-specific subspaces; (ii) a sparse mixing loss that regularizes the mixing structure via sparsity-inducing priors; and (iii) a zero-reconstruction loss that ensures orthogonality and interpretability of recovered sources. The model is trained end-to-end to independently estimate and separate constituent sources from their nonlinear mixture. Extensive evaluation on synthetic data and real-world multichannel polysomnography (PSG) recordings—specifically electrocardiogram (ECG) and photoplethysmogram (PPG)-derived respiratory signals—demonstrates substantial performance gains over conventional single-channel BSS approaches. This work establishes a new paradigm for physiological signal disentanglement.
📝 Abstract
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.