  Cycle-consistent generative adversarial networks (CycleGAN) were successfully
applied to speech enhancement (SE) tasks with unpaired noisy-clean training
data. The CycleGAN SE system adopted two generators and two discriminators
trained with losses from noisy-to-clean and clean-to-noisy conversions.
CycleGAN showed promising results for numerous SE tasks. Herein, we investigate
a potential limitation of the clean-to-noisy conversion part and propose a
novel noise-informed training (NIT) approach to improve the performance of the
original CycleGAN SE system. The main idea of the NIT approach is to
incorporate target domain information for clean-to-noisy conversion to
facilitate a better training procedure. The experimental results confirmed that
the proposed NIT approach improved the generalization capability of the
original CycleGAN SE system with a notable margin.
