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 1–10 of 107 papers

TitleStatusHype
Music Source RestorationCode1
Training-Free Multi-Step Audio Source SeparationCode2
Is MixIT Really Unsuitable for Correlated Sources? Exploring MixIT for Unsupervised Pre-training in Music Source Separation—0
Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration Deep Learning Pipeline—0
Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical MusicCode0
Separate This, and All of these Things Around It: Music Source Separation via Hyperellipsoidal Queries—0
Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music—0
MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation—0
Learned Compression for Compressed LearningCode0
Music Foundation Model as Generic Booster for Music Downstream Tasks—0
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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