  Non-autoregressive GAN-based neural vocoders are widely used due to their
fast inference speed and high perceptual quality. However, they often suffer
from audible artifacts such as tonal artifacts in their generated results.
Therefore, we propose JenGAN, a new training strategy that involves stacking
shifted low-pass filters to ensure the shift-equivariant property. This method
helps prevent aliasing and reduce artifacts while preserving the model
structure used during inference. In our experimental evaluation, JenGAN
consistently enhances the performance of vocoder models, yielding significantly
superior scores across the majority of evaluation metrics.
