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 21–30 of 112 papers

TitleStatusHype
Parallel and Flexible Sampling from Autoregressive Models via Langevin DynamicsCode1
Differentiable Model Compression via Pseudo Quantization NoiseCode1
Compute and memory efficient universal sound source separationCode1
Directional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech MixturesCode1
Unified Gradient Reweighting for Model Biasing with Applications to Source SeparationCode1
The Cone of Silence: Speech Separation by LocalizationCode1
AutoClip: Adaptive Gradient Clipping for Source Separation NetworksCode1
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
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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