marytts.signalproc.adaptation.outlier

Class KMeansMappingEliminatorParams

java.lang.Object

marytts.signalproc.adaptation.BaselineParams

marytts.signalproc.adaptation.outlier.BaselineOutlierEliminatorParams

marytts.signalproc.adaptation.outlier.KMeansMappingEliminatorParams

public class KMeansMappingEliminatorParams
extends BaselineOutlierEliminatorParams

This class implements a K-Means clustering and mapping based outlier elimination procedure: - Step1: Cluster source and target acoustic features either jointly or separately - Step2: For each feature, for each source cluster find the most likely target cluster - Step3: For each feature, for each target cluster find the most likely source cluster - Step4: Determine outlier pairs by checking the total number of source-target pairs assigned to clusters other than the most likely cluster which are sufficiently "distant" from the most likely cluster

Author:

Oytun Türk

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_NUM_CLUSTERS
int	distanceType
static int	ELIMINATE_LEAST_LIKELY_MAPPINGS
static int	ELIMINATE_MEAN_DISTANCE_MISMATCHES
static int	ELIMINATE_USING_SUBCLUSTER_MEAN_DISTANCES
int	eliminationAlgorithm
double	eliminationLikelihood
boolean	isDiagonalCovariance
boolean	isGlobalVariance
boolean	isSeparateClustering
int	maxIterations
double	minClusterChangePercent
int	numClusters
int	numClustersDuration
int	numClustersEnergy
int	numClustersF0
int	numClustersLsf
TotalStandardDeviations	totalStandardDeviations

Fields inherited from class marytts.signalproc.adaptation.outlier.BaselineOutlierEliminatorParams

isActive, isCheckDurationOutliers, isCheckEnergyOutliers, isCheckF0Outliers, isCheckLsfOutliers

Constructor Summary

Constructors

Constructor and Description
KMeansMappingEliminatorParams()
KMeansMappingEliminatorParams(KMeansMappingEliminatorParams existing)

Method Summary

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

eliminationAlgorithm

public int eliminationAlgorithm

ELIMINATE_LEAST_LIKELY_MAPPINGS

public static final int ELIMINATE_LEAST_LIKELY_MAPPINGS

See Also:

Constant Field Values

ELIMINATE_MEAN_DISTANCE_MISMATCHES

public static final int ELIMINATE_MEAN_DISTANCE_MISMATCHES

See Also:

Constant Field Values

ELIMINATE_USING_SUBCLUSTER_MEAN_DISTANCES

public static final int ELIMINATE_USING_SUBCLUSTER_MEAN_DISTANCES

See Also:

Constant Field Values

eliminationLikelihood

public double eliminationLikelihood

totalStandardDeviations

public TotalStandardDeviations totalStandardDeviations

distanceType

public int distanceType

isGlobalVariance

public boolean isGlobalVariance

isSeparateClustering

public boolean isSeparateClustering

numClusters

public int numClusters

numClustersLsf

public int numClustersLsf

numClustersF0

public int numClustersF0

numClustersDuration

public int numClustersDuration

numClustersEnergy

public int numClustersEnergy

DEFAULT_NUM_CLUSTERS

public static final int DEFAULT_NUM_CLUSTERS

See Also:

Constant Field Values

maxIterations

public int maxIterations

minClusterChangePercent

public double minClusterChangePercent

isDiagonalCovariance

public boolean isDiagonalCovariance

Constructor Detail

KMeansMappingEliminatorParams

public KMeansMappingEliminatorParams()

KMeansMappingEliminatorParams

public KMeansMappingEliminatorParams(KMeansMappingEliminatorParams existing)