Uses of Class
comirva.audio.util.math.Matrix

Packages that use Matrix

comirva.audio.extraction
comirva.audio.feature
comirva.audio.util
comirva.audio.util.gmm
comirva.audio.util.kmeans
comirva.audio.util.math

Uses of Matrix in comirva.audio.extraction

Methods in comirva.audio.extraction that return Matrix

protected Matrix	SpectralPatternCentExtractor.createPattern(double[][] block) Computes the fluctuation pattern for a short piece of audio.
protected Matrix	FluctuationPatternExtractor.createPattern(double[][] sone) Computes the fluctuation pattern for a short piece of audio.
protected Matrix	FluctuationPatternCentExtractor.createPattern(double[][] spectrum) Computes the fluctuation pattern for a short piece of audio.
protected Matrix	SpectralPatternCentExtractor.getSpectralPatterns() Splits the audio stream in short segments and computes a fluctuation pattern for every third segment.

Methods in comirva.audio.extraction that return types with arguments of type Matrix

protected Vector<Matrix>	FluctuationPatternExtractor.getFluctuationPatterns() Splits the audio stream in short segments and computes a fluctuation pattern for every third segment.
protected Vector<Matrix>	FluctuationPatternCentExtractor.getFluctuationPatterns() Splits the audio stream in short segments and computes a fluctuation pattern for every third segment.

Uses of Matrix in comirva.audio.feature

Fields in comirva.audio.feature declared as Matrix

protected Matrix	MandelEllis.covarMatrix
protected Matrix	MandelEllis.covarMatrixInv the inverted covarMatrix, stored for computational efficiency
protected Matrix	SpectralPatternCent.m
protected Matrix	FluctuationPatternCent.m
protected Matrix	FluctuationPattern.m
protected Matrix	MandelEllis.mean a row vector

Methods in comirva.audio.feature that return Matrix

Matrix	SpectralPatternCent.getMatrix() Returns the fluctuation pattern matrix.
Matrix	FluctuationPatternCent.getMatrix() Returns the fluctuation pattern matrix.
Matrix	FluctuationPattern.getMatrix() Returns the fluctuation pattern matrix.

Constructors in comirva.audio.feature with parameters of type Matrix

FluctuationPattern(Matrix m)
A matrix describing the rhythmic structure of a song in various frequency bands known as fluctuation patterns.

FluctuationPatternCent(Matrix m)
A matrix describing the rhythmic structure of a song in various frequency bands known as fluctuation patterns.

MandelEllis(Matrix covarMatrix, Matrix mean)
"Gaussian Mixture Model for Mandel / Ellis algorithm" This class holds the features needed for the Mandel Ellis algorithm: One full covariance matrix, and the mean of all MFCCS.

SpectralPatternCent(Matrix m)
A matrix describing the rhythmic structure of a song in various frequency bands known as fluctuation patterns.

Uses of Matrix in comirva.audio.util

Fields in comirva.audio.util declared as Matrix

protected Matrix[]

PointList.data

Methods in comirva.audio.util that return Matrix

Matrix	PointList.get(int i) Returns the i-th point in this list.
Matrix	PointList.getMean() Returns a vector containing the means of each coordinate.
Matrix	PointList.getVariance() Returns a vector containing the variance of each coordinate.

Uses of Matrix in comirva.audio.util.gmm

Methods in comirva.audio.util.gmm that return Matrix

Matrix	GaussianComponent.getMean() For testing purpose only.
Matrix	GaussianMixture.getMean(int numberOfComponent) For testing purpose only.

Methods in comirva.audio.util.gmm with parameters of type Matrix

double	GaussianMixture.getProbability(Matrix x) Returns the probability of a single sample point under the assumption that it was draw from the distribution represented by this GMM.
double	GaussianComponent.getWeightedSampleProbability(Matrix x) Returns the probability of drawing the given sample from this n-dimensional gaussian distribution weighted with the prior probability of this component.

Constructors in comirva.audio.util.gmm with parameters of type Matrix

GaussianComponent(double componentWeight, Matrix mean, Matrix covariances)
Creates a gaussian component and checks the component settings for correctness.

GaussianMixture(double[] componentWeights, Matrix[] means, Matrix[] covariances)
This constructor creates a GMM and checks the parameters for plausibility.

GaussianMixture(double[] componentWeights, Matrix[] means, Matrix[] covariances)
This constructor creates a GMM and checks the parameters for plausibility.

Uses of Matrix in comirva.audio.util.kmeans

Fields in comirva.audio.util.kmeans declared as Matrix

protected Matrix	KMeansClustering.Cluster.center
protected Matrix[]	KMeansClustering.covariances

Methods in comirva.audio.util.kmeans that return Matrix

Matrix	KMeansClustering.Cluster.getCenter() Returns the cluster center.
Matrix	KMeansClustering.getDiagCovarianceMatrix(int cluster) Returns the diagonal covariance matrix of the specified cluster.
Matrix[]	KMeansClustering.getDiagCovariances() Returns the diagonal covaraince matrices of all clusters in one array.
Matrix	KMeansClustering.getFullCovarianceMatrix(int cluster) Returns the full covariance matrix of the specified cluster.
Matrix[]	KMeansClustering.getFullCovariances() Returns the full covaraince matrices of all clusters in one array.
Matrix	KMeansClustering.getMean(int cluster) Returns the mean of the specified cluster.
Matrix	KMeansClustering.Cluster.getMeanOfElements() Returns the mean of all the elements in this cluster.
Matrix[]	KMeansClustering.getMeans() Returns the mean vectors of all clusters in one array.
Matrix	KMeansClustering.Cluster.getVarianceOfElements() Returns the variance of all the elements in this cluster.

Methods in comirva.audio.util.kmeans with parameters of type Matrix

void	KMeansClustering.Cluster.add(Matrix x) Adds a point x to this cluster.
double	KMeansClustering.Cluster.getDistanceFromCenter(Matrix x) Returns the euclidian distance of a point x to the cluster center.
void	KMeansClustering.Cluster.reset(Matrix newCenter) Resets all internal values of this cluster.

Constructors in comirva.audio.util.kmeans with parameters of type Matrix

KMeansClustering.Cluster(Matrix mean)
Constructs a new cluster.

Uses of Matrix in comirva.audio.util.math

Methods in comirva.audio.util.math that return Matrix

Matrix	Matrix.abs() returns a new Matrix object, where each value is set to the absolute value
Matrix	Matrix.arrayLeftDivide(Matrix B) Element-by-element left division, C = A.
Matrix	Matrix.arrayLeftDivideEquals(Matrix B) Element-by-element left division in place, A = A.
Matrix	Matrix.arrayRightDivide(Matrix B) Element-by-element right division, C = A.
Matrix	Matrix.arrayRightDivideEquals(Matrix B) Element-by-element right division in place, A = A.
Matrix	Matrix.arrayTimes(Matrix B) Element-by-element multiplication, C = A.
Matrix	Matrix.arrayTimesEquals(Matrix B) Element-by-element multiplication in place, A = A.
static Matrix	Matrix.constructWithCopy(double[][] A) Construct a matrix from a copy of a 2-D array.
Matrix	NormalizedConvolution.convolute(Matrix A, boolean firstDimension) Perform the convolution of the given matrix according to the specified dimension.
Matrix	Matrix.copy() Make a deep copy of a matrix
Matrix	Matrix.cov() Calculate the full covariance matrix.
Matrix	EigenvalueDecomposition.getD() Return the block diagonal eigenvalue matrix
Matrix	QRDecomposition.getH() Return the Householder vectors
Matrix	LUDecomposition.getL() Return lower triangular factor
Matrix	CholeskyDecomposition.getL() Return triangular factor.
Matrix	Matrix.getMatrix(int[] r, int[] c) Get a submatrix.
Matrix	Matrix.getMatrix(int[] r, int j0, int j1) Get a submatrix.
Matrix	Matrix.getMatrix(int i0, int i1, int[] c) Get a submatrix.
Matrix	Matrix.getMatrix(int i0, int i1, int j0, int j1) Get a submatrix.
Matrix	QRDecomposition.getQ() Generate and return the (economy-sized) orthogonal factor
Matrix	QRDecomposition.getR() Return the upper triangular factor
Matrix	SingularValueDecomposition.getS() Return the diagonal matrix of singular values
Matrix	SingularValueDecomposition.getU() Return the left singular vectors
Matrix	LUDecomposition.getU() Return upper triangular factor
Matrix	SingularValueDecomposition.getV() Return the right singular vectors
Matrix	EigenvalueDecomposition.getV() Return the eigenvector matrix
static Matrix	Matrix.identity(int m, int n) Generate identity matrix
Matrix	Matrix.inverse() Matrix inverse or pseudoinverse
Matrix	Matrix.mean(int dim) Returns the mean values along the specified dimension.
Matrix	Matrix.minus(Matrix B) C = A - B
Matrix	Matrix.minusEquals(Matrix B) A = A - B
Matrix	Matrix.plus(Matrix B) C = A + B
Matrix	Matrix.plusEquals(Matrix B) A = A + B
Matrix	Matrix.pow(double exp) X.powEquals() calculates the power of each element of the matrix.
static Matrix	Matrix.random(int m, int n) Generate matrix with random elements
static Matrix	Matrix.readCSV(InputStream inputStream)
Matrix	QRDecomposition.solve(Matrix B) Least squares solution of A*X = B
Matrix	Matrix.solve(Matrix B) Solve A*X = B
Matrix	LUDecomposition.solve(Matrix B) Solve A*X = B
Matrix	CholeskyDecomposition.solve(Matrix B) Solve A*X = B
Matrix	Matrix.solveTranspose(Matrix B) Solve X*A = B, which is also A'*X' = B'
Matrix	Matrix.times(double s) Multiply a matrix by a scalar, C = s*A
Matrix	Matrix.times(Matrix B) Linear algebraic matrix multiplication, A * B
Matrix	Matrix.timesEquals(double s) Multiply a matrix by a scalar in place, A = s*A
Matrix	Matrix.timesTriangular(Matrix B) Linear algebraic matrix multiplication, A * B B being a triangular matrix Note: Actually the matrix should be a column orienten, upper triangular matrix but use the row oriented, lower triangular matrix instead (transposed), because this is faster due to the easyer array access.
Matrix	Matrix.transpose() Matrix transpose.
Matrix	Matrix.uminus() Unary minus

Methods in comirva.audio.util.math with parameters of type Matrix

Matrix	Matrix.arrayLeftDivide(Matrix B) Element-by-element left division, C = A.
Matrix	Matrix.arrayLeftDivideEquals(Matrix B) Element-by-element left division in place, A = A.
Matrix	Matrix.arrayRightDivide(Matrix B) Element-by-element right division, C = A.
Matrix	Matrix.arrayRightDivideEquals(Matrix B) Element-by-element right division in place, A = A.
Matrix	Matrix.arrayTimes(Matrix B) Element-by-element multiplication, C = A.
Matrix	Matrix.arrayTimesEquals(Matrix B) Element-by-element multiplication in place, A = A.
Matrix	NormalizedConvolution.convolute(Matrix A, boolean firstDimension) Perform the convolution of the given matrix according to the specified dimension.
Matrix	Matrix.minus(Matrix B) C = A - B
Matrix	Matrix.minusEquals(Matrix B) A = A - B
Matrix	Matrix.plus(Matrix B) C = A + B
Matrix	Matrix.plusEquals(Matrix B) A = A + B
void	Matrix.setMatrix(int[] r, int[] c, Matrix X) Set a submatrix.
void	Matrix.setMatrix(int[] r, int j0, int j1, Matrix X) Set a submatrix.
void	Matrix.setMatrix(int i0, int i1, int[] c, Matrix X) Set a submatrix.
void	Matrix.setMatrix(int i0, int i1, int j0, int j1, Matrix X) Set a submatrix.
Matrix	QRDecomposition.solve(Matrix B) Least squares solution of A*X = B
Matrix	Matrix.solve(Matrix B) Solve A*X = B
Matrix	LUDecomposition.solve(Matrix B) Solve A*X = B
Matrix	CholeskyDecomposition.solve(Matrix B) Solve A*X = B
Matrix	Matrix.solveTranspose(Matrix B) Solve X*A = B, which is also A'*X' = B'
Matrix	Matrix.times(Matrix B) Linear algebraic matrix multiplication, A * B
Matrix	Matrix.timesTriangular(Matrix B) Linear algebraic matrix multiplication, A * B B being a triangular matrix Note: Actually the matrix should be a column orienten, upper triangular matrix but use the row oriented, lower triangular matrix instead (transposed), because this is faster due to the easyer array access.

Constructors in comirva.audio.util.math with parameters of type Matrix

CholeskyDecomposition(Matrix Arg)
Cholesky algorithm for symmetric and positive definite matrix.

EigenvalueDecomposition(Matrix Arg)
Check for symmetry, then construct the eigenvalue decomposition

LUDecomposition(Matrix A)
LU Decomposition

QRDecomposition(Matrix A)
QR Decomposition, computed by Householder reflections.

SingularValueDecomposition(Matrix Arg)
Construct the singular value decomposition