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.

翻译：本文证明，一维条件生成对抗网络（cGAN）采用对抗训练架构可实现非配对的信号到信号翻译。通过使用简化的CycleGAN模型（包含一维层和更宽卷积核，仿照WaveGAN将二维图像生成重构为一维音频生成），展示了在未对传统U-Net模型和CycleGAN提出的对抗架构进行实质性修改的情况下，即可将二维图像到图像翻译任务重构为一维信号到信号翻译任务。基于小型可调数据集的研究表明：未经配对的训练、未被一维CycleGAN模型观测的含噪测试信号，从源域转换后生成与目标域配对测试信号相似的信号（尤其在频率特性方面），并通过相关系数和误差量化了这些差异。