  Despite the rapid progress in speech enhancement (SE) research, enhancing the
quality of desired speech in environments with strong noise and interfering
speakers remains challenging. In this paper, we extend the application of the
recently proposed band-split RNN (BSRNN) model to full-band SE and personalized
SE (PSE) tasks. To mitigate the effects of unstable high-frequency components
in full-band speech, we perform bi-directional and uni-directional band-level
modeling to low-frequency and high-frequency subbands, respectively. For PSE
task, we incorporate a speaker enrollment module into BSRNN to utilize target
speaker information. Moreover, we utilize a MetricGAN discriminator (MGD) and a
multi-resolution spectrogram discriminator (MRSD) to improve perceptual quality
metrics. Experimental results show that our system outperforms various
top-ranking SE systems, achieves state-of-the-art (SOTA) results on the
DNS-2020 test set and ranks among the top 3 in the DNS-2023 challenge.
