  Modern audio source separation techniques rely on optimizing sequence model
architectures such as, 1D-CNNs, on mixture recordings to generalize well to
unseen mixtures. Specifically, recent focus is on time-domain based
architectures such as Wave-U-Net which exploit temporal context by extracting
multi-scale features. However, the optimality of the feature extraction process
in these architectures has not been well investigated. In this paper, we
examine and recommend critical architectural changes that forge an optimal
multi-scale feature extraction process. To this end, we replace regular $1-$D
convolutions with adaptive dilated convolutions that have innate capability of
capturing increased context by using large temporal receptive fields. We also
investigate the impact of dense connections on the extraction process that
encourage feature reuse and better gradient flow. The dense connections between
the downsampling and upsampling paths of a U-Net architecture capture
multi-resolution information leading to improved temporal modelling. We
evaluate the proposed approaches on the MUSDB test dataset. In addition to
providing an improved performance over the state-of-the-art, we also provide
insights on the impact of different architectural choices on complex
data-driven solutions for source separation.
