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While traditional statistical signal processing model-based methods canderive the optimal estimators relying on specific statistical assumptions,current learning-based methods further promote the performance upper bound viadeep neural networks but at the expense of high encapsulation and lack adequateinterpretability. Standing upon the intersection between traditionalmodel-based methods and learning-based methods, we propose a model-drivenapproach based on the maximum a posteriori (MAP) framework, termed as MDNet,for single-channel speech enhancement. Specifically, the original problem isformulated into the joint posterior estimation w.r.t. speech and noisecomponents. Different from the manual assumption toward the prior terms, wepropose to model the prior distribution via networks and thus can learn fromtraining data. The framework takes the unfolding structure and in each step,the target parameters can be progressively estimated through explicit gradientdescent operations. Besides, another network serves as the fusion module tofurther refine the previous speech estimation. The experiments are conducted onthe WSJ0-SI84 and Interspeech2020 DNS-Challenge datasets, and quantitativeresults show that the proposed approach outshines previous state-of-the-artbaselines.

MDNet: Learning Monaural Speech Enhancement from Deep Prior Gradient