  In self-supervised learning, it is challenging to reduce the gap between the
enhancement performance on the estimated and target speech signals with existed
pre-tasks. In this paper, we propose a multi-task pre-training method to
improve the speech enhancement performance with self-supervised learning.
Within the pre-training autoencoder (PAE), only a limited set of clean speech
signals are required to learn their latent representations. Meanwhile, to solve
the limitation of single pre-task, the proposed masking module exploits the
dereverberated mask and estimated ratio mask to denoise the mixture as the
second pre-task. Different from the PAE, where the target speech signals are
estimated, the downstream task autoencoder (DAE) utilizes a large number of
unlabeled and unseen reverberant mixtures to generate the estimated mixtures.
The trained DAE is shared by the learned representations and masks.
Experimental results on a benchmark dataset demonstrate that the proposed
method outperforms the state-of-the-art approaches.
