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

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Showing 51–100 of 107 papers

TitleStatusHype
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
Feature-informed Latent Space Regularization for Music Source Separation—0
Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation—0
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation—0
Hybrid Y-Net Architecture for Singing Voice Separation—0
Improving Real-Time Music Accompaniment Separation with MMDenseNet—0
Is MixIT Really Unsuitable for Correlated Sources? Exploring MixIT for Unsupervised Pre-training in Music Source Separation—0
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training—0
MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation—0
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement—0
Multi-scale temporal-frequency attention for music source separation—0
Multitask learning for instrument activation aware music source separation—0
Music Foundation Model as Generic Booster for Music Downstream Tasks—0
Music Separation Enhancement with Generative Modeling—0
On loss functions and evaluation metrics for music source separation—0
Pac-HuBERT: Self-Supervised Music Source Separation via Primitive Auditory Clustering and Hidden-Unit BERT—0
Pre-trained Spatial Priors on Multichannel NMF for Music Source Separation—0
Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet—0
Distortion Audio Effects: Learning How to Recover the Clean Signal—0
Resource-constrained stereo singing voice cancellation—0
Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music—0
Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data—0
Separate This, and All of these Things Around It: Music Source Separation via Hyperellipsoidal Queries—0
Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration Deep Learning Pipeline—0
Source Separation and Depthwise Separable Convolutions for Computer Audition—0
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation—0
Spectrogram Feature Losses for Music Source Separation—0
Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference—0
Task-Aware Unified Source Separation—0
Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision—0
Upsampling layers for music source separation—0
Voice and accompaniment separation in music using self-attention convolutional neural network—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Music Source Separation in the Waveform DomainCode0
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice ExtractionCode0
Music source separation conditioned on 3D point cloudsCode0
Sams-Net: A Sliced Attention-based Neural Network for Music Source SeparationCode0
Multi-scale Multi-band DenseNets for Audio Source SeparationCode0
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image ModelsCode0
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant MethodCode0
Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical MusicCode0
Low Latency Time Domain Multichannel Speech and Music Source SeparationCode0
Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic MixturesCode0
Learned Compression for Compressed LearningCode0
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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