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 51–60 of 112 papers

TitleStatusHype
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
Sams-Net: A Sliced Attention-based Neural Network for Music Source SeparationCode0
Text-Queried Audio Source Separation via Hierarchical Modeling—0
Towards Listening to 10 People Simultaneously: An Efficient Permutation Invariant Training of Audio Source Separation Using Sinkhorn's Algorithm—0
Weakly-supervised Audio-visual Sound Source Detection and Separation—0
WildMix Dataset and Spectro-Temporal Transformer Model for Monoaural Audio Source Separation—0
ZeroSep: Separate Anything in Audio with Zero Training—0
Adversarial attacks on audio source separation—0
Zero-shot Audio Source Separation through Query-based Learningfrom Weakly-labeled Data—0
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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