  Generally, the performance of deep neural networks (DNNs) heavily depends on
the quality of data representation learning. Our preliminary work has
emphasized the significance of deep representation learning (DRL) in the
context of speech enhancement (SE) applications. Specifically, our initial SE
algorithm employed a gated recurrent unit variational autoencoder (VAE) with a
Gaussian distribution to enhance the performance of certain existing SE
systems. Building upon our preliminary framework, this paper introduces a novel
approach for SE using deep complex convolutional recurrent networks with a VAE
(DCCRN-VAE). DCCRN-VAE assumes that the latent variables of signals follow
complex Gaussian distributions that are modeled by DCCRN, as these
distributions can better capture the behaviors of complex signals.
Additionally, we propose the application of a residual loss in DCCRN-VAE to
further improve the quality of the enhanced speech. {Compared to our
preliminary work, DCCRN-VAE introduces a more sophisticated DCCRN structure and
probability distribution for DRL. Furthermore, in comparison to DCCRN,
DCCRN-VAE employs a more advanced DRL strategy. The experimental results
demonstrate that the proposed SE algorithm outperforms both our preliminary SE
framework and the state-of-the-art DCCRN SE method in terms of scale-invariant
signal-to-distortion ratio, speech quality, and speech intelligibility.
