SOTAVerified

Audio Source Separation

Audio Source Separation is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals).

Source: Model selection for deep audio source separation via clustering analysis

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–112 of 112 papers

TitleStatusHype
Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction TasksCode0
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant MethodCode0
Co-Separating Sounds of Visual ObjectsCode0
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-DomainCode0
Retrieving Signals in the Frequency Domain with Deep Complex ExtractorsCode0
Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source SeparationsCode0
Audio-Visual Scene Analysis with Self-Supervised Multisensory FeaturesCode0
Music source separation conditioned on 3D point cloudsCode0
Visual Scene Graphs for Audio Source SeparationCode0
A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix FactorizationCode0
Audio Source Separation Using Variational Autoencoders and Weak Class SupervisionCode0
Sams-Net: A Sliced Attention-based Neural Network for Music Source SeparationCode0
Show:102550
← PrevPage 5 of 5Next →
All datasetsAudioSetMUSIC (multi-source)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-SED-SEPSDR10.55—Unverified
2Co-SeparationSDR4.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-SeparationSAR11.3—Unverified