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
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
Generalization Challenges for Neural Architectures in Audio Source SeparationCode0
Training Generative Adversarial Networks from Incomplete Observations using Factorised DiscriminatorsCode0
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
A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation—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