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The harmonic structure of speech is resistant to noise, but the harmonics maystill be partially masked by noise. Therefore, we previously proposed aharmonic gated compensation network (HGCN) to predict the full harmoniclocations based on the unmasked harmonics and process the result of a coarseenhancement module to recover the masked harmonics. In addition, the auditoryloudness loss function is used to train the network. For the DNS Challenge, weupdate HGCN with the following aspects, resulting in HGCN+. First, a high-bandmodule is employed to help the model handle full-band signals. Second, cosineis used to model the harmonic structure more accurately. Then, the dual-pathencoder and dual-path rnn (DPRNN) are introduced to take full advantage of thefeatures. Finally, a gated residual linear structure replaces the gatedconvolution in the compensation module to increase the receptive field offrequency. The experimental results show that each updated module bringsperformance improvement to the model. HGCN+ also outperforms the referencedmodels on both wide-band and full-band test sets.

Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE