Music Source Separation
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Greatest papers with code
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
• f90/Wave-U-Net
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Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end.
End-to-end music source separation: is it possible in the waveform domain?
• francesclluis/source-separation-wavenet
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Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase.
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
• f90/AdversarialAudioSeparation
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Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples.
Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures
We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music.
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence
Non-negative matrix factorization (NMF) approximates a given matrix as a product of two non-negative matrices.