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
Reduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks—0
Visual Scene Graphs for Audio Source SeparationCode0
Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction TasksCode0
Move2Hear: Active Audio-Visual Source Separation—0
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant MethodCode0
MULTIMODAL ANALYSIS: Informed content estimation and audio source separation—0
Weakly-supervised Audio-visual Sound Source Detection and Separation—0
Music source separation conditioned on 3D point cloudsCode0
Densely connected multidilated convolutional networks for dense prediction tasksCode0
Problems using deep generative models for probabilistic audio source separation—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