Ziang Xiao, Sarah Mennicken, Bernd Huber, Adam Shonkoff, Jennifer Thom
Singing Voice Separation with Deep U-Net Convolutional Networks
The decomposition of a music audio signal into its vocal and backing track components is analogous to image-toimage translation, where a mixed spectrogram is transformed into its constituent sources. We propose a novel application of the U-Net architecture — initially developed for medical imaging — for the task of source separation, given its proven capacity for recreating the fine, low-level detail required for high-quality audio reproduction. Through both quantitative evaluation and subjective assessment, experiments demonstrate that the proposed algorithm achieves state-of-the-art performance.
Judith Bütepage, Lucas Maystre, Mounia Lalmas
Brianna Richardson, Jean Garcia-Gathright, Samuel F. Way, Jennifer Thom, Henriette Cramer