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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
S^3 -- Semantic Signal SeparationCode2
Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational EnvironmentsCode2
Unsupervised Composable Representations for AudioCode1
Blind Source Separation of Single-Channel Mixtures via Multi-Encoder AutoencodersCode1
GPU-accelerated Guided Source Separation for Meeting TranscriptionCode1
Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike TrainsCode1
Unrolling PALM for sparse semi-blind source separationCode1
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection SeparationCode1
Drum-Aware Ensemble Architecture for Improved Joint Musical Beat and Downbeat TrackingCode1
Independent mechanism analysis, a new concept?Code1
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