  The harmonic structure of speech is resistant to noise, but the harmonics may
still be partially masked by noise. Therefore, we previously proposed a
harmonic gated compensation network (HGCN) to predict the full harmonic
locations based on the unmasked harmonics and process the result of a coarse
enhancement module to recover the masked harmonics. In addition, the auditory
loudness loss function is used to train the network. For the DNS Challenge, we
update HGCN with the following aspects, resulting in HGCN+. First, a high-band
module is employed to help the model handle full-band signals. Second, cosine
is used to model the harmonic structure more accurately. Then, the dual-path
encoder and dual-path rnn (DPRNN) are introduced to take full advantage of the
features. Finally, a gated residual linear structure replaces the gated
convolution in the compensation module to increase the receptive field of
frequency. The experimental results show that each updated module brings
performance improvement to the model. HGCN+ also outperforms the referenced
models on both wide-band and full-band test sets.
