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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 110 of 211 papers

TitleStatusHype
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
A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection RemovalCode0
Revisiting convolutive blind source separation for identifying spiking motor neuron activity: From theory to practiceCode0
Low-Rank Matrix Factorizations with Volume-based Constraints and Regularizations0
High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning0
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