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.


August 2020 | KDD

Bandit based Optimization of Multiple Objectives on a Music Streaming Platform

Rishabh Mehrotra, Niannan Xue, Mounia Lalmas

August 2020 | Uncertainty in Artificial Intelligence (UAI)

Stochastic Variational Inference for Dynamic Correlated Topic Models

Federico Tomasi, Praveen Chandar, Gal Levy-Fix, Mounia Lalmas, Zhenwen Dai