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Complex spectrum and magnitude are considered as two major features of speechenhancement and dereverberation. Traditional approaches always treat these twofeatures separately, ignoring their underlying relationship. In this paper, wepropose Uformer, a Unet based dilated complex & real dual-path conformernetwork in both complex and magnitude domain for simultaneous speechenhancement and dereverberation. We exploit time attention (TA) and dilatedconvolution (DC) to leverage local and global contextual information andfrequency attention (FA) to model dimensional information. These threesub-modules contained in the proposed dilated complex & real dual-pathconformer module effectively improve the speech enhancement and dereverberationperformance. Furthermore, hybrid encoder and decoder are adopted tosimultaneously model the complex spectrum and magnitude and promote theinformation interaction between two domains. Encoder decoder attention is alsoapplied to enhance the interaction between encoder and decoder. Ourexperimental results outperform all SOTA time and complex domain modelsobjectively and subjectively. Specifically, Uformer reaches 3.6032 DNSMOS onthe blind test set of Interspeech 2021 DNS Challenge, which outperforms alltop-performed models. We also carry out ablation experiments to tease apart allproposed sub-modules that are most important.

Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation