Score-based Source Separation with Applications to Digital Communication Signals
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911759143534592 |
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| author | Jayashankar, Tejas Lee, Gary C. F. Lancho, Alejandro Weiss, Amir Polyanskiy, Yury Wornell, Gregory W. |
| author_facet | Jayashankar, Tejas Lee, Gary C. F. Lancho, Alejandro Weiss, Amir Polyanskiy, Yury Wornell, Gregory W. |
| contents | 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. The project webpage is available at https://alpha-rgs.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_14411 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Score-based Source Separation with Applications to Digital Communication Signals Jayashankar, Tejas Lee, Gary C. F. Lancho, Alejandro Weiss, Amir Polyanskiy, Yury Wornell, Gregory W. Machine Learning Signal Processing 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. The project webpage is available at https://alpha-rgs.github.io |
| title | Score-based Source Separation with Applications to Digital Communication Signals |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2306.14411 |