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 26–50 of 112 papers

TitleStatusHype
Directional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech MixturesCode1
Unsupervised Source Separation By Steering Pretrained Music ModelsCode1
Facing the Music: Tackling Singing Voice Separation in Cinematic Audio Source SeparationCode1
Compute and memory efficient universal sound source separationCode1
The Cone of Silence: Speech Separation by LocalizationCode1
A Generalized Bandsplit Neural Network for Cinematic Audio Source SeparationCode1
Parallel and Flexible Sampling from Autoregressive Models via Langevin DynamicsCode1
Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision—0
Fast accuracy estimation of deep learning based multi-class musical source separation—0
Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF—0
A Study of Transfer Learning in Music Source Separation—0
Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds—0
Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for Audio Source Separation—0
Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset—0
A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation—0
On loss functions and evaluation metrics for music source separation—0
DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization—0
Adversarial attacks on audio source separation—0
Interleaved Multitask Learning for Audio Source Separation with Independent Databases—0
Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation—0
Language-Guided Audio-Visual Source Separation via Trimodal Consistency—0
Densely connected multidilated convolutional networks for dense prediction tasks—0
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation—0
Improving Universal Sound Separation Using Sound Classification—0
Improved Speech Enhancement with the Wave-U-Net—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