  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.
