blind source separation
44 papers with code • 0 benchmarks • 0 datasets
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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Use these libraries to find blind source separation models and implementationsMost implemented papers
Sifting Common Information from Many Variables
Measuring the relationship between any pair of variables is a rich and active area of research that is central to scientific practice.
Variational Mixture Models with Gamma or inverse-Gamma components
Mixture models with Gamma and or inverse-Gamma distributed mixture components are useful for medical image tissue segmentation or as post-hoc models for regression coefficients obtained from linear regression within a Generalised Linear Modeling framework (GLM), used in this case to separate stochastic (Gaussian) noise from some kind of positive or negative "activation" (modeled as Gamma or inverse-Gamma distributed).
Blind Source Separation Using Mixtures of Alpha-Stable Distributions
We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions.
Trace your sources in large-scale data: one ring to find them all
An important preprocessing step in most data analysis pipelines aims to extract a small set of sources that explain most of the data.
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
We demonstrate the ability of CRsAE to recover the underlying dictionary and characterize its sensitivity as a function of SNR.
Semi-blind source separation with multichannel variational autoencoder
mori97/MVAE
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This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture.
On the achievability of blind source separation for high-dimensional nonlinear source mixtures
This work theoretically validates that a cascade of linear PCA and ICA can solve a nonlinear BSS problem accurately -- when the sensory inputs are generated from hidden sources via nonlinear mappings with sufficient dimensionality.
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count Estimation
faroit/CountNet
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IEEE/ACM Transactions on Audio, Speech, and Language Processing 2018
Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance.
Time Series Source Separation using Dynamic Mode Decomposition
We show that when the latent time series are uncorrelated at a lag of one time-step then, in the large sample limit, the recovered dynamic modes will approximate, up to a column-wise normalization, the columns of the mixing matrix.
Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS).
