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 81–90 of 112 papers

TitleStatusHype
Retrieving Signals in the Frequency Domain with Deep Complex ExtractorsCode0
Retrieving Signals with Deep Complex Extractors—0
Sams-Net: A Sliced Attention-based Neural Network for Music Source SeparationCode0
Interleaved Multitask Learning for Audio Source Separation with Independent Databases—0
Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF—0
Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source SeparationsCode0
A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix FactorizationCode0
Generalized Separable Nonnegative Matrix Factorization—0
Training Generative Adversarial Networks from Incomplete Observations using Factorised DiscriminatorsCode0
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