Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas
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.
Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming Sessions
Aaron Ng, Rishabh Mehrotra
Rishabh Mehrotra, Niannan Xue, Mounia Lalmas