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
Adversarial attacks on audio source separation—0
Multi-scale Multi-band DenseNets for Audio Source SeparationCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice ExtractionCode0
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source SeparationCode0
Training Generative Adversarial Networks from Incomplete Observations using Factorised DiscriminatorsCode0
J-Net: Randomly weighted U-Net for audio source separationCode0
Improved Speech Enhancement with the Wave-U-NetCode0
Generalization Challenges for Neural Architectures in Audio Source SeparationCode0
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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