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 51–75 of 107 papers

TitleStatusHype
Multi-scale Multi-band DenseNets for Audio Source SeparationCode0
Music Source Separation in the Waveform DomainCode0
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image ModelsCode0
Learned Compression for Compressed LearningCode0
Music source separation conditioned on 3D point cloudsCode0
Low Latency Time Domain Multichannel Speech and Music Source SeparationCode0
D3Net: Densely connected multidilated DenseNet for music source separationCode0
Sams-Net: A Sliced Attention-based Neural Network for Music Source SeparationCode0
On loss functions and evaluation metrics for music source separation—0
Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation—0
A frugal approach to music source separation—0
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation—0
An Ensemble Approach to Music Source Separation: A Comparative Analysis of Conventional and Hierarchical Stem Separation—0
A Study of Transfer Learning in Music Source Separation—0
Bootstrapping deep music separation from primitive auditory grouping principles—0
Class-conditional embeddings for music source separation—0
Contrastive Learning based Deep Latent Masking for Music Source Separation—0
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity—0
Deep Clustering and Conventional Networks for Music Separation: Stronger Together—0
Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation—0
Depthwise Separable Convolutions Versus Recurrent Neural Networks for Monaural Singing Voice Separation—0
Dilated Convolution with Dilated GRU for Music Source Separation—0
End-to-End Sound Source Separation Conditioned On Instrument Labels—0
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation—0
Fast accuracy estimation of deep learning based multi-class musical source separation—0
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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