Paper accepted at NeurIPS 2023!
Our paper, “Score-based Source Separation with Applications to Digital Communication Signals,” has been accepted for poster presentation at NeurIPS 2023.
👥 Authors: Tejas Jayashankar, Gary C.F. Lee, Alejandro Lancho, Amir Weiss, Yury Polyanskiy, Gregory W. Wornell
📑 Abstract: We propose a new method for separating superimposed sources using diffusion-based generative models. Our method relies only on separately trained statistical priors of independent sources to establish a new objective function guided by maximum a posteriori estimation with an α-posterior, across multiple levels of Gaussian smoothing. Motivated by applications in radio-frequency (RF) systems, we are interested in sources with underlying discrete nature and the recovery of encoded bits from a signal of interest, as measured by the bit error rate (BER). Experimental results with RF mixtures demonstrate that our method results in a BER reduction of 95% over classical and existing learning-based methods. Our analysis demonstrates that our proposed method yields solutions that asymptotically approach the modes of an underlying discrete distribution. Furthermore, our method can be viewed as a multi-source extension to the recently proposed score distillation sampling scheme, shedding additional light on its use beyond conditional sampling.
📝 If interested, the paper is already available on arxiv.