基于一维条件GAN的（非）配对信号到信号翻译
(Un)paired signal-to-signal translation with 1D conditional GANs
AI总结：
本研究提出基于1D条件GAN的非配对sig2sig翻译方法，借鉴WaveGAN改造简化版CycleGAN，无需大幅改动原有架构即可实现1D信号翻译，在小型数据集上验证了含噪信号的域转换效果并量化差异。
摘要：
本文证明，具备对抗训练架构的一维（1D）条件生成对抗网络（cGAN）可实现非配对信号到信号（“sig2sig”）翻译。研究采用带有1D层和更宽卷积核的简化CycleGAN模型，借鉴WaveGAN将二维（2D）图像生成重构为1D音频生成的思路，证实无需对CycleGAN所采用的传统U-Net模型及对抗架构做大幅修改，即可将2D图像到图像的翻译任务重铸为基于深度卷积GAN的1D信号到信号翻译任务。在一个小型可调数据集上的实验表明，1D CycleGAN模型未见过的、无配对训练的含噪测试信号可从源域转换为翻译域内与配对测试信号相似的信号，尤其在频率维度表现突出；本文还通过相关性和误差指标量化了这些差异。
英文摘要：
I show that a one-dimensional (1D) conditional generative adversarial network (cGAN) with an adversarial training architecture is capable of unpaired signal-to-signal ("sig2sig") translation. Using a simplified CycleGAN model with 1D layers and wider convolutional kernels, mirroring WaveGAN to reframe two-dimensional (2D) image generation as 1D audio generation, I show that recasting the 2D image-to-image translation task to a 1D signal-to-signal translation task with deep convolutional GANs is possible without substantial modification to the conventional U-Net model and adversarial architecture developed as CycleGAN. With this I show for a small tunable dataset that noisy test signals unseen by the 1D CycleGAN model and without paired training transform from the source domain to signals similar to paired test signals in the translated domain, especially in terms of frequency, and I quantify these differences in terms of correlation and error.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
