  Generative adversarial networks (GAN) have recently been shown to be
efficient for speech enhancement. However, most, if not all, existing speech
enhancement GANs (SEGAN) make use of a single generator to perform one-stage
enhancement mapping. In this work, we propose to use multiple generators that
are chained to perform multi-stage enhancement mapping, which gradually refines
the noisy input signals in a stage-wise fashion. Furthermore, we study two
scenarios: (1) the generators share their parameters and (2) the generators'
parameters are independent. The former constrains the generators to learn a
common mapping that is iteratively applied at all enhancement stages and
results in a small model footprint. On the contrary, the latter allows the
generators to flexibly learn different enhancement mappings at different stages
of the network at the cost of an increased model size. We demonstrate that the
proposed multi-stage enhancement approach outperforms the one-stage SEGAN
baseline, where the independent generators lead to more favorable results than
the tied generators. The source code is available at
http://github.com/pquochuy/idsegan.
