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 31–40 of 112 papers

TitleStatusHype
A Generalized Bandsplit Neural Network for Cinematic Audio Source SeparationCode1
OtoWorld: Towards Learning to Separate by Learning to MoveCode1
Sams-Net: A Sliced Attention-based Neural Network for Music Source SeparationCode0
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-DomainCode0
Audio-Visual Scene Analysis with Self-Supervised Multisensory FeaturesCode0
Densely connected multidilated convolutional networks for dense prediction tasksCode0
Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction TasksCode0
A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix FactorizationCode0
Retrieving Signals in the Frequency Domain with Deep Complex ExtractorsCode0
Co-Separating Sounds of Visual ObjectsCode0
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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