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 110 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 Separation0
Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration Deep Learning Pipeline0
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 Queries0
Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music0
MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation0
Learned Compression for Compressed LearningCode0
Music Foundation Model as Generic Booster for Music Downstream Tasks0
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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