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

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Showing 91–100 of 112 papers

TitleStatusHype
Co-Separating Sounds of Visual ObjectsCode0
Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets—0
Improved Speech Enhancement with the Wave-U-NetCode0
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks—0
Audio Source Separation Using Variational Autoencoders and Weak Class SupervisionCode0
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-DomainCode0
Improved Speech Enhancement with the Wave-U-Net—0
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation—0
Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation—0
Audio-Visual Scene Analysis with Self-Supervised Multisensory FeaturesCode0
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All datasetsAudioSetMUSIC (multi-source)

Benchmark Results

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