  PercepNet, a recent extension of the RNNoise, an efficient, high-quality and
real-time full-band speech enhancement technique, has shown promising
performance in various public deep noise suppression tasks. This paper proposes
a new approach, named PercepNet+, to further extend the PercepNet with four
significant improvements. First, we introduce a phase-aware structure to
leverage the phase information into PercepNet, by adding the complex features
and complex subband gains as the deep network input and output respectively.
Then, a signal-to-noise ratio (SNR) estimator and an SNR switched
post-processing are specially designed to alleviate the over attenuation (OA)
that appears in high SNR conditions of the original PercepNet. Moreover, the
GRU layer is replaced by TF-GRU to model both temporal and frequency
dependencies. Finally, we propose to integrate the loss of complex subband
gain, SNR, pitch filtering strength, and an OA loss in a multi-objective
learning manner to further improve the speech enhancement performance.
Experimental results show that, the proposed PercepNet+ outperforms the
original PercepNet significantly in terms of both PESQ and STOI, without
increasing the model size too much.
