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
Multi-Resolution Fully Convolutional Neural Networks for Monaural 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
Adversarial attacks on audio source separation—0
DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization—0
Language-Guided Audio-Visual Source Separation via Trimodal Consistency—0
Improving Universal Sound Separation Using Sound Classification—0
Improved Speech Enhancement with the Wave-U-Net—0
Audio Source Separation with Discriminative Scattering Networks—0
Hyperbolic 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