Music Source Separation
( Image credit: SigSep
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.
#3 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.
#5 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.
#2 best model 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.
#4 best model for
Music Source Separation
on MUSDB18
Meta-learning Extractors for Music Source Separation
• pfnet-research/meta-tasnet
•
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models.
SOTA 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.