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 76–100 of 112 papers

TitleStatusHype
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation—0
Generalized Separable Nonnegative Matrix Factorization—0
Gull: A Generative Multifunctional Audio Codec—0
Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation—0
Hyperbolic Audio Source Separation—0
Improved Speech Enhancement with the Wave-U-Net—0
Improving Universal Sound Separation Using Sound Classification—0
Independent Deeply Learned Matrix Analysis for Multichannel Audio Source Separation—0
Interleaved Multitask Learning for Audio Source Separation with Independent Databases—0
Language-Guided Audio-Visual Source Separation via Trimodal Consistency—0
Low algorithmic delay implementation of convolutional beamformer for online joint source separation and dereverberation—0
Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation—0
Model selection for deep audio source separation via clustering analysis—0
Move2Hear: Active Audio-Visual Source Separation—0
MULTIMODAL ANALYSIS: Informed content estimation and audio source separation—0
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation—0
Nonnegative Tensor Factorization for Directional Blind Audio Source Separation—0
On loss functions and evaluation metrics for music source separation—0
Performance Improvement of Language-Queried Audio Source Separation Based on Caption Augmentation From Large Language Models for DCASE Challenge 2024 Task 9—0
Problems using deep generative models for probabilistic audio source separation—0
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders—0
Reduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks—0
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks—0
Retrieving Signals with Deep Complex Extractors—0
Sampling Frequency Independent Dialogue 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