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.

Benchmarks

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Most implemented papers

Sequence-to-point learning with neural networks for nonintrusive load monitoring

Content

Directional Sparse Filtering Using Weighted Lehmer Mean for Blind Separation of Unbalanced Speech Mixtures

Sparse pursuit and dictionary learning for blind source separation in polyphonic music recordings

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

GPU-accelerated Guided Source Separation for Meeting Transcription

Sparse and Non-Negative BSS for Noisy Data

NMF with Sparse Regularizations in Transformed Domains

Sparsity and Adaptivity for the Blind Separation of Partially Correlated Sources

Latent Bayesian melding for integrating individual and population models