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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 patternsdoi:10.1109/waspaa.2019.8937079 dblp:conf/waspaa/LordeloBDA19 fatcat:2ormcydurvcl3ohif56dgpvvda