en.unionpedia.org

Signal separation, the Glossary

Index Signal separation

Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process.[1]

Table of Contents

  1. 41 relations: Adaptive filter, Auditory scene analysis, Basis (linear algebra), Canonical correlation, Cocktail party, Cocktail party effect, Colin Cherry, Common spatial pattern, Computational auditory scene analysis, Correlation, Deconvolution, Dependent component analysis, Digital image, Digital signal processing, Electroencephalography, Electromagnetic field, Factorial code, Image segmentation, Independence (probability theory), Independent component analysis, Infomax, Information theory, Joint Approximation Diagonalization of Eigen-matrices, Magnetic field, Magnetoencephalography, Medical imaging, Multidimensional analysis, Music, Non-negative matrix factorization, Principal component analysis, Python (programming language), Shogun (toolbox), Signal, Signal processing, Singular value decomposition, Sonic artifact, Sparse matrix, Speech segmentation, Stationary subspace analysis, Tensor, Underdetermined system.

  2. Speech processing

Adaptive filter

An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimization algorithm. Signal separation and adaptive filter are digital signal processing.

See Signal separation and Adaptive filter

Auditory scene analysis

In perception and psychophysics, auditory scene analysis (ASA) is a proposed model for the basis of auditory perception.

See Signal separation and Auditory scene analysis

Basis (linear algebra)

In mathematics, a set of vectors in a vector space is called a basis (bases) if every element of may be written in a unique way as a finite linear combination of elements of.

See Signal separation and Basis (linear algebra)

Canonical correlation

In statistics, canonical-correlation analysis (CCA), also called canonical variates analysis, is a way of inferring information from cross-covariance matrices.

See Signal separation and Canonical correlation

Cocktail party

A cocktail party is a party at which cocktails are served.

See Signal separation and Cocktail party

Cocktail party effect

The cocktail party effect refers to a phenomenon wherein the brain focuses a person's attention on a particular stimulus, usually auditory.

See Signal separation and Cocktail party effect

Colin Cherry

Edward Colin Cherry (23 June 1914 – 23 November 1979) was a British cognitive scientist whose main contributions were in focused auditory attention, specifically the cocktail party problem regarding the capacity to follow one conversation while many other conversations are going on in a noisy room.

See Signal separation and Colin Cherry

Common spatial pattern

Common spatial pattern (CSP) is a mathematical procedure used in signal processing for separating a multivariate signal into additive subcomponents which have maximum differences in variance between two windows.

See Signal separation and Common spatial pattern

Computational auditory scene analysis

Computational auditory scene analysis (CASA) is the study of auditory scene analysis by computational means. Signal separation and computational auditory scene analysis are digital signal processing.

See Signal separation and Computational auditory scene analysis

Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data.

See Signal separation and Correlation

Deconvolution

In mathematics, deconvolution is the inverse of convolution.

See Signal separation and Deconvolution

Dependent component analysis

Dependent component analysis (DCA) is a blind signal separation (BSS) method and an extension of Independent component analysis (ICA).

See Signal separation and Dependent component analysis

Digital image

A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with x, y on the x-axis and y-axis, respectively.

See Signal separation and Digital image

Digital signal processing

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations.

See Signal separation and Digital signal processing

Electroencephalography

Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain.

See Signal separation and Electroencephalography

Electromagnetic field

An electromagnetic field (also EM field) is a physical field, mathematical functions of position and time, representing the influences on and due to electric charges.

See Signal separation and Electromagnetic field

Factorial code

Most real world data sets consist of data vectors whose individual components are not statistically independent.

See Signal separation and Factorial code

Image segmentation

In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels).

See Signal separation and Image segmentation

Independence (probability theory)

Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.

See Signal separation and Independence (probability theory)

Independent component analysis

In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents.

See Signal separation and Independent component analysis

Infomax

Infomax is an optimization principle for artificial neural networks and other information processing systems.

See Signal separation and Infomax

Information theory

Information theory is the mathematical study of the quantification, storage, and communication of information.

See Signal separation and Information theory

Joint Approximation Diagonalization of Eigen-matrices

Joint Approximation Diagonalization of Eigen-matrices (JADE) is an algorithm for independent component analysis that separates observed mixed signals into latent source signals by exploiting fourth order moments.

See Signal separation and Joint Approximation Diagonalization of Eigen-matrices

Magnetic field

A magnetic field (sometimes called B-field) is a physical field that describes the magnetic influence on moving electric charges, electric currents, and magnetic materials.

See Signal separation and Magnetic field

Magnetoencephalography

Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers.

See Signal separation and Magnetoencephalography

Medical imaging

Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology).

See Signal separation and Medical imaging

Multidimensional analysis

In statistics, econometrics and related fields, multidimensional analysis (MDA) is a data analysis process that groups data into two categories: data dimensions and measurements.

See Signal separation and Multidimensional analysis

Music

Music is the arrangement of sound to create some combination of form, harmony, melody, rhythm, or otherwise expressive content.

See Signal separation and Music

Non-negative matrix factorization

Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra where a matrix is factorized into (usually) two matrices and, with the property that all three matrices have no negative elements.

See Signal separation and Non-negative matrix factorization

Principal component analysis

Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.

See Signal separation and Principal component analysis

Python (programming language)

Python is a high-level, general-purpose programming language.

See Signal separation and Python (programming language)

Shogun is a free, open-source machine learning software library written in C++.

See Signal separation and Shogun (toolbox)

Signal

Signal refers to both the process and the result of transmission of data over some media accomplished by embedding some variation. Signal separation and Signal are digital signal processing.

See Signal separation and Signal

Signal processing

Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements.

See Signal separation and Signal processing

Singular value decomposition

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a rescaling followed by another rotation.

See Signal separation and Singular value decomposition

Sonic artifact

In sound and music production, sonic artifact, or simply artifact, refers to sonic material that is accidental or unwanted, resulting from the editing or manipulation of a sound.

See Signal separation and Sonic artifact

Sparse matrix

In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero.

See Signal separation and Sparse matrix

Speech segmentation

Speech segmentation is the process of identifying the boundaries between words, syllables, or phonemes in spoken natural languages.

See Signal separation and Speech segmentation

Stationary subspace analysis

Stationary Subspace Analysis (SSA) von Bünau P, Meinecke F C, Király F J, Müller K-R (2009).

See Signal separation and Stationary subspace analysis

Tensor

In mathematics, a tensor is an algebraic object that describes a multilinear relationship between sets of algebraic objects related to a vector space.

See Signal separation and Tensor

Underdetermined system

In mathematics, a system of linear equations or a system of polynomial equations is considered underdetermined if there are fewer equations than unknowns (in contrast to an overdetermined system, where there are more equations than unknowns).

See Signal separation and Underdetermined system

See also

Speech processing

References

[1] https://en.wikipedia.org/wiki/Signal_separation

Also known as Blind signal separation, Blind source separation, Multivariate Curve Resolution, Self-modeling mixture analysis.