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 51–60 of 112 papers

TitleStatusHype
A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation—0
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
Densely connected multidilated convolutional networks for dense prediction tasks—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