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
Improving Universal Sound Separation Using Sound Classification—0
Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation—0
Interleaved Multitask Learning for Audio Source Separation with Independent Databases—0
Language-Guided Audio-Visual Source Separation via Trimodal Consistency—0
Low algorithmic delay implementation of convolutional beamformer for online joint source separation and dereverberation—0
Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation—0
Model selection for deep audio source separation via clustering analysis—0
Move2Hear: Active Audio-Visual Source Separation—0
MULTIMODAL ANALYSIS: Informed content estimation and audio source separation—0
SepIt: Approaching a Single Channel Speech Separation Bound—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