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 41–50 of 112 papers

TitleStatusHype
On loss functions and evaluation metrics for music source separation—0
DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization—0
Adversarial attacks on audio source separation—0
Interleaved Multitask Learning for Audio Source Separation with Independent Databases—0
Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation—0
Language-Guided Audio-Visual Source Separation via Trimodal Consistency—0
Densely connected multidilated convolutional networks for dense prediction tasks—0
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation—0
Improving Universal Sound Separation Using Sound Classification—0
Improved Speech Enhancement with the Wave-U-Net—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