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 31–40 of 112 papers

TitleStatusHype
A Generalized Bandsplit Neural Network for Cinematic Audio Source SeparationCode1
Multi-Task Audio Source SeparationCode1
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
Show:102550
← PrevPage 4 of 12Next →
All datasetsAudioSetMUSIC (multi-source)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-SED-SEPSDR10.55—Unverified
2Co-SeparationSDR4.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-SeparationSAR11.3—Unverified