SOTAVerified

Music Source Separation

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 107 papers

TitleStatusHype
Hybrid Transformers for Music Source SeparationCode5
The Whole Is Greater than the Sum of Its Parts: Improving Music Source Separation by Bridging NetworkCode4
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech SeparationCode3
A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four StemsCode2
All for One and One for All: Improving Music Separation by Bridging NetworksCode2
Pre-training Music Classification Models via Music Source SeparationCode2
SCNet: Sparse Compression Network for Music Source SeparationCode2
Music Source Separation Based on a Lightweight Deep Learning Framework (DTTNET: DUAL-PATH TFC-TDF UNET)Code2
The Sound Demixing Challenge 2023 x2013 Music Demixing TrackCode2
Training-Free Multi-Step Audio Source SeparationCode2
LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source SeparationCode1
Meta-learning Extractors for Music Source SeparationCode1
A fully differentiable model for unsupervised singing voice separationCode1
Time-Domain Audio Source Separation Based on Wave-U-Net Combined with Discrete Wavelet TransformCode1
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio EffectsCode1
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source SeparationCode1
MedleyVox: An Evaluation Dataset for Multiple Singing Voices SeparationCode1
Unsupervised Music Source Separation Using Differentiable Parametric Source ModelsCode1
KUIELab-MDX-Net: A Two-Stream Neural Network for Music DemixingCode1
Music Demixing Challenge 2021Code1
Multi-channel U-Net for Music Source SeparationCode1
Transfer Learning with Jukebox for Music Source SeparationCode1
Danna-Sep: Unite to separate them allCode1
VocaLiST: An Audio-Visual Synchronisation Model for Lips and VoicesCode1
A Unified Model for Zero-shot Music Source Separation, Transcription and SynthesisCode1
End-to-end music source separation: is it possible in the waveform domain?Code1
Unsupervised Interpretable Representation Learning for Singing Voice SeparationCode1
Quantifying Spatial Audio Quality ImpairmentCode1
Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3Code1
Multi-Task Audio Source SeparationCode1
Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source SeparationCode1
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained ModelsCode1
Music Source Separation with Band-split RNNCode1
A cappella: Audio-visual Singing Voice SeparationCode1
Music Source Separation with Generative FlowCode1
CWS-PResUNet: Music Source Separation with Channel-wise Subband Phase-aware ResUNetCode1
An Efficient Short-Time Discrete Cosine Transform and Attentive MultiResUNet Framework for Music Source SeparationCode1
Music Source RestorationCode1
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source SeparationCode1
Unsupervised Source Separation via Bayesian Inference in the Latent DomainCode1
Fast accuracy estimation of deep learning based multi-class musical source separation—0
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation—0
Class-conditional embeddings for music source separation—0
End-to-End Sound Source Separation Conditioned On Instrument Labels—0
Bootstrapping deep music separation from primitive auditory grouping principles—0
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation—0
Dilated Convolution with Dilated GRU for Music Source Separation—0
Depthwise Separable Convolutions Versus Recurrent Neural Networks for Monaural Singing Voice Separation—0
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement—0
A Study of Transfer Learning in Music Source Separation—0
Show:102550
← PrevPage 1 of 3Next →
All datasetsMUSDB18MUSDB18-HQSlakh2100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Sparse HT Demucs (fine tuned)SDR (avg)9.2—Unverified
2Hybrid Transformer Demucs (f.t.)SDR (avg)9—Unverified
3Band-Split RNN (semi-sup.)SDR (avg)8.97—Unverified
4TFC-TDF-UNet (v3)SDR (avg)8.34—Unverified
5Band-Split RNNSDR (avg)8.23—Unverified
6Hybrid DemucsSDR (avg)7.72—Unverified
7KUIELab-MDX-NetSDR (avg)7.54—Unverified
8CDE-HTCNSDR (avg)6.89—Unverified
9Attentive-MultiResUNetSDR (avg)6.81—Unverified
10DEMUCS (extra)SDR (avg)6.79—Unverified
#ModelMetricClaimedVerifiedStatus
1BS-RoFormer (L=12, OA)SDR (avg)11.99—Unverified
2BS-RoFormer (L=6, OA)SDR (avg)9.8—Unverified
3SCNet-largeSDR (avg)9.69—Unverified
4Sparse HT Demucs (fine tuned)SDR (avg)9.2—Unverified
5SCNetSDR (avg)9—Unverified
6Hybrid Transformer Demucs (f.t.)SDR (avg)9—Unverified
7Band-Split RNN (semi-sup.)SDR (avg)8.97—Unverified
8TFC-TDF-UNet (v3)SDR (avg)8.34—Unverified
9Band-Split RNNSDR (avg)8.24—Unverified
10Dual-Path TFC-TDF UNet (DTTNet)SDR (avg)8.15—Unverified
#ModelMetricClaimedVerifiedStatus
1DiCoSe (Deterministic)SI-SDRi (Bass)20.04—Unverified
2LQ-VAE + Scalable TransformerSDR (bass)7.42—Unverified