  Mask processing in the time-frequency (T-F) domain through the neural network
has been one of the mainstreams for single-channel speech enhancement. However,
it is hard for most models to handle the situation when harmonics are partially
masked by noise. To tackle this challenge, we propose a harmonic gated
compensation network (HGCN). We design a high-resolution harmonic integral
spectrum to improve the accuracy of harmonic locations prediction. Then we add
voice activity detection (VAD) and voiced region detection (VRD) to the
convolutional recurrent network (CRN) to filter harmonic locations. Finally,
the harmonic gating mechanism is used to guide the compensation model to adjust
the coarse results from CRN to obtain the refinedly enhanced results. Our
experiments show HGCN achieves substantial gain over a number of advanced
approaches in the community.
