  Deep learning-based music source separation has gained a lot of interest in
the last decades. Most of the existing methods operate with either spectrograms
or waveforms. Spectrogram based models learn suitable masks for separating
magnitude spectrogram into different sources, and waveform-based models
directly generate waveforms of individual sources. The two types of models have
complementary strengths; the former is superior given harmonic sources such as
vocals, while the latter demonstrates better results for percussion and bass
instruments. In this work, we improved upon the state-of-the-art (SoTA) models
and successfully combined the best of both worlds. The backbones of the
proposed framework, dubbed Danna-Sep, are two spectrogram-based models
including a modified X-UMX and U-Net, and an enhanced Demucs as the
waveform-based model. Given an input of mixture, we linearly combined
respective outputs from the three models to obtain the final result. We showed
in the experiments that, despite its simplicity, Danna-Sep surpassed the SoTA
models by a large margin in terms of Source-to-Distortion Ratio.
