Music Source Separation
( Image credit: Open-Unmix )
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed
We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments.
#2 best model for
Music Source Separation
on MUSDB18
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
• f90/Wave-U-Net
• 
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.
#4 best model for
Music Source Separation
on MUSDB18
Open-Unmix - A Reference Implementation for Music Source Separation
The Journal of Open Source Software 2019
• sigsep/open-unmix-pytorch
• 
Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.
SOTA for Music Source Separation on MUSDB18
End-to-end music source separation: is it possible in the waveform domain?
• francesclluis/source-separation-wavenet
•
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.
#3 best model for
Music Source Separation
on MUSDB18
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
• f90/AdversarialAudioSeparation
•
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.
Multi-scale Multi-band DenseNets for Audio Source Separation
This paper deals with the problem of audio source separation.
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.