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blind source separation
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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.
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Blind Source Separation in Biomedical Signals Using Variational Methods
Spatial Speech Translation: Translating Across Space With Binaural Hearables
HyperKING: Quantum-Classical Generative Adversarial Networks for Hyperspectral Image Restoration
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
Joint Spectrogram Separation and TDOA Estimation using Optimal Transport
A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal
Revisiting convolutive blind source separation for identifying spiking motor neuron activity: From theory to practice
Low-Rank Matrix Factorizations with Volume-based Constraints and Regularizations
Enhancing Blind Source Separation with Dissociative Principal Component Analysis
High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning
Distributed Blind Source Separation based on FastICA
MotionLeaf: Fine-grained Multi-Leaf Damped Vibration Monitoring for Plant Water Stress using Low-Cost mmWave Sensors
Unsupervised Composable Representations for Audio
PRIME: Blind Multispectral Unmixing Using Virtual Quantum Prism and Convex Geometry
$S^3$ -- Semantic Signal Separation
Neural Blind Source Separation and Diarization for Distant Speech Recognition
Nonparametric Evaluation of Noisy ICA Solutions
Online Similarity-and-Independence-Aware Beamformer for Low-latency Target Sound Extraction
A computationally efficient semi-blind source separation based approach for nonlinear echo cancellation based on an element-wise iterative source steering
MultiView Independent Component Analysis with Delays
How secure is the time-modulated array-enabled ofdm directional modulation?
An Exploration of Optimal Parameters for Efficient Blind Source Separation of EEG Recordings Using AMICA
Quantifying Non-linear Dependencies in Blind Source Separation of Power System Signals using Copula Statistics
Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Framework
Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders
EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis
EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation
Source Identification: A Self-Supervision Task for Dense Prediction
Ongoing EEG artifact correction using blind source separation
Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation
AudioSlots: A slot-centric generative model for audio separation
A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function
A Robustness Analysis of Blind Source Separation
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model
Identification of Power System Oscillation Modes using Blind Source Separation based on Copula Statistic
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