  While traditional statistical signal processing model-based methods can
derive the optimal estimators relying on specific statistical assumptions,
current learning-based methods further promote the performance upper bound via
deep neural networks but at the expense of high encapsulation and lack adequate
interpretability. Standing upon the intersection between traditional
model-based methods and learning-based methods, we propose a model-driven
approach based on the maximum a posteriori (MAP) framework, termed as MDNet,
for single-channel speech enhancement. Specifically, the original problem is
formulated into the joint posterior estimation w.r.t. speech and noise
components. Different from the manual assumption toward the prior terms, we
propose to model the prior distribution via networks and thus can learn from
training data. The framework takes the unfolding structure and in each step,
the target parameters can be progressively estimated through explicit gradient
descent operations. Besides, another network serves as the fusion module to
further refine the previous speech estimation. The experiments are conducted on
the WSJ0-SI84 and Interspeech2020 DNS-Challenge datasets, and quantitative
results show that the proposed approach outshines previous state-of-the-art
baselines.
