In this work, we treat the task of signal denoising as distribution alignment between the clean and noisy signal. An adversarial encoder-decoder network is proposed for denoising signals, represented by a sequence of measurements. We rely on the signal's latent representation, given by the encoder, to detect clean and noisy samples. Aligning the two signal distributions results in removing the noise. Unlike the standard GAN training, we propose a new formulation that suits to one-dimensional signal denoising. In the evaluation, we show better performance than the related approaches, such as autoencoders, wavenet denoiser and recurrent neural networks, demonstrating the benefits of our approach in different signal and noise types.     «

In this work, we treat the task of signal denoising as distribution alignment between the clean and noisy signal. An adversarial encoder-decoder network is proposed for denoising signals, represented by a sequence of measurements. We rely on the signal's latent representation, given by the encoder, to detect clean and noisy samples. Aligning the two signal distributions results in removing the noise. Unlike the standard GAN training, we propose a new formulation that suits to one-dimensional sig...     »