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 1–10 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
Show:102550
← PrevPage 1 of 11Next →
All datasetsMUSDB18MUSDB18-HQSlakh2100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DiCoSe (Deterministic)SI-SDRi (Bass)20.04—Unverified
2LQ-VAE + Scalable TransformerSDR (bass)7.42—Unverified