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Music Source Separation Using Stacked Hourglass Networks
2018
Zenodo
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
doi:10.5281/zenodo.1492404
fatcat:dosh3dtjdnforatu2rs6svwize