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 61–70 of 112 papers

TitleStatusHype
Densely connected multidilated convolutional networks for dense prediction tasksCode0
Problems using deep generative models for probabilistic audio source separation—0
Unified Gradient Reweighting for Model Biasing with Applications to Source SeparationCode1
A Study of Transfer Learning in Music Source Separation—0
Towards Listening to 10 People Simultaneously: An Efficient Permutation Invariant Training of Audio Source Separation Using Sinkhorn's Algorithm—0
Fast accuracy estimation of deep learning based multi-class musical source separation—0
The Cone of Silence: Speech Separation by LocalizationCode1
Adversarial attacks on audio source separation—0
Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio—0
AutoClip: Adaptive Gradient Clipping for Source Separation NetworksCode1
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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