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 5175 of 107 papers

TitleStatusHype
Distortion Audio Effects: Learning How to Recover the Clean Signal0
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
KUIELab-MDX-Net: A Two-Stream Neural Network for Music DemixingCode1
Upsampling layers for music source separation0
Hybrid Spectrogram and Waveform Source SeparationCode0
Unsupervised Source Separation via Bayesian Inference in the Latent DomainCode1
Music Demixing Challenge 2021Code1
A Unified Model for Zero-shot Music Source Separation, Transcription and SynthesisCode1
Multi-Task Audio Source SeparationCode1
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation0
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant MethodCode0
A cappella: Audio-visual Singing Voice SeparationCode1
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation0
Music source separation conditioned on 3D point cloudsCode0
Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation0
Source Separation and Depthwise Separable Convolutions for Computer Audition0
Densely connected multidilated convolutional networks for dense prediction tasksCode0
A Study of Transfer Learning in Music Source Separation0
LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source SeparationCode1
Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision0
Fast accuracy estimation of deep learning based multi-class musical source separation0
All for One and One for All: Improving Music Separation by Bridging NetworksCode2
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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
#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