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 71–80 of 112 papers

TitleStatusHype
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
Adversarial attacks on audio source separation—0
Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio—0
Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices—0
J-Net: Randomly weighted U-Net for audio source separationCode0
WildMix Dataset and Spectro-Temporal Transformer Model for Monoaural Audio Source Separation—0
Improving Universal Sound Separation Using Sound Classification—0
Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision—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