  In this paper, we propose a simple yet effective method for multiple music
source separation using convolutional neural networks. Stacked hourglass
network, which was originally designed for human pose estimation in natural
images, is applied to a music source separation task. The network learns
features from a spectrogram image across multiple scales and generates masks
for each music source. The estimated mask is refined as it passes over stacked
hourglass modules. The proposed framework is able to separate multiple music
sources using a single network. Experimental results on MIR-1K and DSD100
datasets validate that the proposed method achieves competitive results
comparable to the state-of-the-art methods in multiple music source separation
and singing voice separation tasks.
