Multi-scale Multi-band DenseNets for Audio Source Separation
Table 2: Comparison of SDR.
['Method', 'SDR in dB Bass', 'SDR in dB Drums', 'SDR in dB Other', 'SDR in dB Vocals', 'SDR in dB Acco.']
Among the state-of-the-art baselines, BLEND showed the best performance, which was a fusion of BLSTM and FNN. MDenseNet performed as good as BLSTM, which also utilized MWF. This suggests that the multi-scale architecture successfully learned to utilize long term contexts using the stack of convolution layers instead of the recurrent architecture. This claim will be further investigated in the next subsection. MMDenseNet significantly improved performance and largely outperformed all baselines, showing the effectiveness of the multi-band architecture. We also trained MMDenseNet with the additional dataset, MedleyDB as FNN approach, and denoted it as MMDenseNet+. It further improved performances for all instruments except drums and showed the best overall result. Notably, we obtained 0.97dB improvement on average over the best results of the SiSEC 2016.