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 51–100 of 112 papers

TitleStatusHype
Learning to Separate Object Sounds by Watching Unlabeled VideoCode0
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
Generalization Challenges for Neural Architectures in Audio Source SeparationCode0
Training Generative Adversarial Networks from Incomplete Observations using Factorised DiscriminatorsCode0
Weakly-supervised Audio-visual Sound Source Detection and Separation—0
WildMix Dataset and Spectro-Temporal Transformer Model for Monoaural Audio Source Separation—0
ZeroSep: Separate Anything in Audio with Zero Training—0
Adversarial attacks on audio source separation—0
Zero-shot Audio Source Separation through Query-based Learningfrom Weakly-labeled Data—0
A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation—0
A Study of Transfer Learning in Music Source Separation—0
Audio Source Separation Using a Deep Autoencoder—0
Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets—0
Audio Source Separation with Discriminative Scattering Networks—0
Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset—0
Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF—0
Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution—0
Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices—0
DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization—0
Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for Audio Source Separation—0
Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds—0
Fast accuracy estimation of deep learning based multi-class musical source separation—0
Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision—0
Fish sounds: towards the evaluation of marine acoustic biodiversity through data-driven audio source separation—0
GASS: Generalizing Audio Source Separation with Large-scale Data—0
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