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 71–80 of 112 papers

TitleStatusHype
Sudo rm -rf: Efficient Networks for Universal Audio Source SeparationCode1
OtoWorld: Towards Learning to Separate by Learning to MoveCode1
Unsupervised Audio Source Separation using Generative PriorsCode1
Time-Domain Audio Source Separation Based on Wave-U-Net Combined with Discrete Wavelet TransformCode1
Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices—0
J-Net: Randomly weighted U-Net for audio source separationCode0
WildMix Dataset and Spectro-Temporal Transformer Model for Monoaural Audio Source Separation—0
Improving Universal Sound Separation Using Sound Classification—0
Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision—0
Model selection for deep audio source separation via clustering analysis—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