  In this paper we propose an efficient deep learning encoder-decoder network
for performing Harmonic-Percussive Source Separation (HPSS). It is shown that
we are able to greatly reduce the number of model trainable parameters by using
a dense arrangement of skip connections between the model layers. We also
explore the utilisation of different kernel sizes for the 2D filters of the
convolutional layers with the objective of allowing the network to learn the
different time-frequency patterns associated with percussive and harmonic
sources more efficiently. The training and evaluation of the separation has
been done using the training and test sets of the MUSDB18 dataset. Results show
that the proposed deep network achieves automatic learning of high-level
features and maintains HPSS performance at a state-of-the-art level while
reducing the number of parameters and training time.
