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 1–10 of 112 papers

TitleStatusHype
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization—0
ZeroSep: Separate Anything in Audio with Zero Training—0
Text-Queried Audio Source Separation via Hierarchical Modeling—0
Training-Free Multi-Step Audio Source SeparationCode2
Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond—0
Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset—0
Study of the Performance of CEEMDAN in Underdetermined Speech Separation—0
Leveraging LLM and Text-Queried Separation for Noise-Robust Sound Event DetectionCode1
Task-Aware Unified Source Separation—0
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All datasetsAudioSetMUSIC (multi-source)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Co-SeparationSAR11.3—Unverified