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blind source separation

Blind source separation (BSS) is a signal processing technique that aims to separate multiple source signals from a set of mixed signals, without any prior knowledge about the sources or the mixing process. The goal is to recover the original source signals from the observed mixtures, typically using statistical and computational methods. BSS has applications in various fields such as audio signal processing, image processing, and telecommunications. It is used to extract useful information from mixed signals and to improve the quality of the source signals.

Papers

Showing 1120 of 211 papers

TitleStatusHype
Beam-Guided TasNet: An Iterative Speech Separation Framework with Multi-Channel OutputCode1
Directional Sparse Filtering using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech MixturesCode1
Fetal ECG Extraction from Maternal ECG using Attention-based CycleGANCode1
Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize PrincipleCode1
Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation ModelsCode0
Blind Source Separation in Biomedical Signals Using Variational Methods0
Spatial Speech Translation: Translating Across Space With Binaural Hearables0
HyperKING: Quantum-Classical Generative Adversarial Networks for Hyperspectral Image Restoration0
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal AnalysisCode0
Joint Spectrogram Separation and TDOA Estimation using Optimal Transport0
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