About
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
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Datasets
Greatest papers with code
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models
ISMIR 2019 Late-Breaking/Demo 2019
• deezer/spleeter
• 
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.
Ranked #8 on
Music Source Separation
on MUSDB18
(using extra training data)
Music Source Separation in the Waveform Domain
Experiments on the MusDB dataset show that Demucs beats previously reported results in terms of signal to distortion ratio (SDR), but lower than Conv-Tasnet.
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.
Ranked #6 on
Music Source Separation
on MUSDB18
(using extra training data)
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.
Ranked #9 on
Music Source Separation
on MUSDB18
MUSIC SOURCE SEPARATION SPEAKER SEPARATION SPEECH ENHANCEMENT SPEECH SEPARATION
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.
Ranked #13 on
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.
Ranked #16 on
Music Source Separation
on MUSDB18
Multi-scale Multi-band DenseNets for Audio Source Separation
This paper deals with the problem of audio source separation.
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.
Ranked #15 on
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.
Ranked #12 on
Music Source Separation
on MUSDB18
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.
Ranked #11 on
Music Source Separation
on MUSDB18
MUSIC SOURCE SEPARATION AUDIO AND SPEECH PROCESSING SOUND