Music Source Separation
53 papers with code • 3 benchmarks • 7 datasets
Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.
( Image credit: SigSep )
Benchmarks
These leaderboards are used to track progress in Music Source Separation
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Libraries
Use these libraries to find Music Source Separation models and implementations
MUSDB18
MedleyDB
Slakh2100
MIR-1K
MUSDB18-HQ
CocoChorales
MuseScore
Most implemented papers
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms.
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.
Multi-scale Multi-band DenseNets for Audio Source Separation
Anjok07/ultimatevocalremovergui
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This paper deals with the problem of audio source separation.
All for One and One for All: Improving Music Separation by Bridging Networks
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.
Music Source Separation with Band-split RNN
amanteur/BandSplitRNN-Pytorch
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The performance of music source separation (MSS) models has been greatly improved in recent years thanks to the development of novel neural network architectures and training pipelines.
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.
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models
deezer/spleeter
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ISMIR 2019 Late-Breaking/Demo 2019
We present and release a new tool for music source separation with pre-trained models called Spleeter. Spleeter was designed with ease of use, separation performance and speed in mind.
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.
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.
Multi-channel U-Net for Music Source Separation
kvsphantom/multitask-unet-bss
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However, Conditioned U-Net (C-U-Net) uses a control mechanism to train a single model for multi-source separation and attempts to achieve a performance comparable to that of the dedicated models.
