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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Music Source Separation models and implementations
MUSDB18
MedleyDB
Slakh2100
MIR-1K
MUSDB18-HQ
CocoChorales
MuseScore
Most implemented papers
A cappella: Audio-visual Singing Voice Separation
JuanFMontesinos/Acappella-YNet
•
•
The task of isolating a target singing voice in music videos has useful applications.
Unsupervised Music Source Separation Using Differentiable Parametric Source Models
schufo/umss
•
•
Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio.
Hybrid Transformers for Music Source Separation
While it performs poorly when trained only on MUSDB, we show that it outperforms Hybrid Demucs (trained on the same data) by 0. 45 dB of SDR when using 800 extra training songs.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
zfturbo/mvsep-mdx23-music-separation-model
•
•
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding1.
Music Source Separation Based on a Lightweight Deep Learning Framework (DTTNET: DUAL-PATH TFC-TDF UNET)
junyuchen-cjy/dttnet-pytorch
•
•
Music source separation (MSS) aims to extract 'vocals', 'drums', 'bass' and 'other' tracks from a piece of mixed music.
Pre-training Music Classification Models via Music Source Separation
cgaroufis/msspt
•
•
In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks.
MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation
Deep neural networks have become an indispensable technique for audio source separation (ASS).
Improving DNN-based Music Source Separation using Phase Features
aRI0U/music-source-separation
•
•
Music source separation with deep neural networks typically relies only on amplitude features.
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.
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.
