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

Showing 125 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
Training-Free Multi-Step Audio Source SeparationCode2
A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four StemsCode2
SCNet: Sparse Compression Network for Music Source SeparationCode2
Pre-training Music Classification Models via 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
All for One and One for All: Improving Music Separation by Bridging NetworksCode2
Music Source RestorationCode1
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source SeparationCode1
A fully differentiable model for unsupervised singing voice separationCode1
Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3Code1
Quantifying Spatial Audio Quality ImpairmentCode1
MedleyVox: An Evaluation Dataset for Multiple Singing Voices SeparationCode1
An Efficient Short-Time Discrete Cosine Transform and Attentive MultiResUNet Framework for Music Source SeparationCode1
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio EffectsCode1
Music Source Separation with Band-split RNNCode1
Music Source Separation with Generative FlowCode1
VocaLiST: An Audio-Visual Synchronisation Model for Lips and VoicesCode1
Unsupervised Music Source Separation Using Differentiable Parametric Source ModelsCode1
CWS-PResUNet: Music Source Separation with Channel-wise Subband Phase-aware ResUNetCode1
Danna-Sep: Unite to separate them allCode1
Transfer Learning with Jukebox for Music Source SeparationCode1
Show:102550
← PrevPage 1 of 5Next →

Benchmark Results

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