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 76–100 of 112 papers

TitleStatusHype
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
Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio—0
Study of the Performance of CEEMDAN in Underdetermined Speech Separation—0
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks—0
Task-Aware Unified Source Separation—0
Text-Queried Audio Source Separation via Hierarchical Modeling—0
Towards Listening to 10 People Simultaneously: An Efficient Permutation Invariant Training of Audio Source Separation Using Sinkhorn's Algorithm—0
Weakly-supervised Audio-visual Sound Source Detection and Separation—0
WildMix Dataset and Spectro-Temporal Transformer Model for Monoaural Audio Source Separation—0
ZeroSep: Separate Anything in Audio with Zero Training—0
Adversarial attacks on audio source separation—0
Multi-scale Multi-band DenseNets for Audio Source SeparationCode0
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source SeparationCode0
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice ExtractionCode0
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
J-Net: Randomly weighted U-Net for audio source separationCode0
Training Generative Adversarial Networks from Incomplete Observations using Factorised DiscriminatorsCode0
Improved Speech Enhancement with the Wave-U-NetCode0
Generalization Challenges for Neural Architectures in Audio Source SeparationCode0
Densely connected multidilated convolutional networks for dense prediction tasksCode0
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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