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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 61–70 of 112 papers

TitleStatusHype
A Study of Transfer Learning in Music Source Separation—0
Audio Source Separation Using a Deep Autoencoder—0
Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets—0
Audio Source Separation with Discriminative Scattering Networks—0
Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset—0
Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF—0
Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution—0
Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices—0
DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization—0
Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for 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