# 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.

## Benchmarks

These leaderboards are used to track progress in blind source separation
## Libraries

Use these libraries to find blind source separation models and implementations## Most implemented papers

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

Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem.

# Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music Recordings

In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset.

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

In blind source separation of speech signals, the inherent imbalance in the source spectrum poses a challenge for methods that rely on single-source dominance for the estimation of the mixing matrix.

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

Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms.

# GPU-accelerated Guided Source Separation for Meeting Transcription

In this paper, we describe our improved implementation of GSS that leverages the power of modern GPU-based pipelines, including batched processing of frequencies and segments, to provide 300x speed-up over CPU-based inference.

# Sparse and Non-Negative BSS for Noisy Data

In this context, it is fundamental that the sources to be estimated present some diversity in order to be efficiently retrieved.

# Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics

We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models.

# NMF with Sparse Regularizations in Transformed Domains

In this article, we show how a sparse NMF algorithm coined non-negative generalized morphological component analysis (nGMCA) can be extended to impose non-negativity in the direct domain along with sparsity in a transformed domain, with both analysis and synthesis formulations.

# Sparsity and adaptivity for the blind separation of partially correlated sources

Blind source separation (BSS) is a very popular technique to analyze multichannel data.

# Latent Bayesian melding for integrating individual and population models

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour.