Multi-scale Multi-band DenseNets for Audio Source Separation
Table 3: Comparison of average SDR, number of parameters and training time per instrument.
['Method', 'avg. SDR', '# of param.', 'training time']
The proposed architecture encourages feature reuse within and between dense blocks, leading to a compact and efficient model. The number of parameters of the proposed architectures are significantly less than the baseline methods. MDenseNet achieved comparable performance to the sate-of-the-art BLEND approach with only 1.5% of the parameters, and MMDenseNet largely outperformed BLEND with only 3.6% of the parameters. This demonstrates the compactness and efficiency of the model, which is preferable for deployment. The training time is also significantly less than for the BLSTM and BLEND methods, making it easier to tune the hyper-parameters.