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
Zero-shot Audio Source Separation through Query-based Learningfrom Weakly-labeled Data—0
Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled DataCode1
Hybrid Neural Networks for On-device Directional HearingCode1
Transfer Learning with Jukebox for Music Source SeparationCode1
Reduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks—0
Unsupervised Source Separation By Steering Pretrained Music ModelsCode1
The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World SoundtracksCode1
Unsupervised Source Separation via Bayesian Inference in the Latent DomainCode1
Visual Scene Graphs for Audio Source SeparationCode0
Multi-Task Audio Source SeparationCode1
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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