  In real acoustic environment, speech enhancement is an arduous task to
improve the quality and intelligibility of speech interfered by background
noise and reverberation. Over the past years, deep learning has shown great
potential on speech enhancement. In this paper, we propose a novel real-time
framework called DBNet which is a dual-branch structure with alternate
interconnection. Each branch incorporates an encoder-decoder architecture with
skip connections. The two branches are responsible for spectrum and waveform
modeling, respectively. A bridge layer is adopted to exchange information
between the two branches. Systematic evaluation and comparison show that the
proposed system substantially outperforms related algorithms under very
challenging environments. And in INTERSPEECH 2021 Deep Noise Suppression (DNS)
challenge, the proposed system ranks the top 8 in real-time track 1 in terms of
the Mean Opinion Score (MOS) of the ITU-T P.835 framework.
