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 91–100 of 112 papers

TitleStatusHype
Move2Hear: Active Audio-Visual Source Separation—0
MULTIMODAL ANALYSIS: Informed content estimation and audio source separation—0
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation—0
Nonnegative Tensor Factorization for Directional Blind Audio Source Separation—0
On loss functions and evaluation metrics for music source separation—0
Performance Improvement of Language-Queried Audio Source Separation Based on Caption Augmentation From Large Language Models for DCASE Challenge 2024 Task 9—0
Problems using deep generative models for probabilistic audio source separation—0
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders—0
Reduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks—0
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks—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