  Deep learning-based methods have made significant achievements in music
source separation. However, obtaining good results while maintaining a low
model complexity remains challenging in super wide-band music source
separation. Previous works either overlook the differences in subbands or
inadequately address the problem of information loss when generating subband
features. In this paper, we propose SCNet, a novel frequency-domain network to
explicitly split the spectrogram of the mixture into several subbands and
introduce a sparsity-based encoder to model different frequency bands. We use a
higher compression ratio on subbands with less information to improve the
information density and focus on modeling subbands with more information. In
this way, the separation performance can be significantly improved using lower
computational consumption. Experiment results show that the proposed model
achieves a signal to distortion ratio (SDR) of 9.0 dB on the MUSDB18-HQ dataset
without using extra data, which outperforms state-of-the-art methods.
Specifically, SCNet's CPU inference time is only 48% of HT Demucs, one of the
previous state-of-the-art models.
