/**
* Clustering.
*
* @param threshold the threshold
* @param clusterSet the cluster set
* @param featureSet the feature set
* @param parameter the parameter
* @return the cluster set
* @throws Exception the exception
*/
public ClusterSet clustering(
double threshold, ClusterSet clusterSet, AudioFeatureSet featureSet, Parameter parameter)
throws Exception {
String mask = parameter.getParameterSegmentationOutputFile().getMask();
String oldMethod = parameter.getParameterClustering().getMethodAsString();
double oldThreshold = parameter.getParameterClustering().getThreshold();
String oldModelKind = parameter.getParameterModel().getModelKindAsString();
int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents();
// --- begin NEW v 1.14 / 4.16 / 4.18 / 4.20---
parameter.getParameterClustering().setMethod("h");
// parameter.getParameterClustering().setMethod("sr");
// --- end NEW v 1.14 ---
parameter.getParameterClustering().setThreshold(threshold);
logger.finer(
"method:"
+ parameter.getParameterClustering().getMethod()
+ " thr:"
+ parameter.getParameterClustering().getThreshold());
parameter.getParameterModel().setModelKind("FULL");
parameter.getParameterModel().setNumberOfComponents(1);
ClusterSet clustersHClust = MClust.make(featureSet, clusterSet, parameter, null);
if (parameter.getParameterDiarization().isSaveAllStep()) {
parameter.getParameterSegmentationOutputFile().setMask(mask + ".h.seg");
MainTools.writeClusterSet(parameter, clustersHClust, false);
}
parameter.getParameterSegmentation().setMethod(oldMethod);
parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent);
parameter.getParameterModel().setModelKind(oldModelKind);
parameter.getParameterClustering().setThreshold(oldThreshold);
parameter.getParameterSegmentationOutputFile().setMask(mask);
return clustersHClust;
}
/**
* Clustering linear.
*
* @param threshold the threshold
* @param clusterSet the cluster set
* @param featureSet the feature set
* @param parameter the parameter
* @return the cluster set
* @throws Exception the exception
*/
public ClusterSet clusteringLinear(
double threshold, ClusterSet clusterSet, AudioFeatureSet featureSet, Parameter parameter)
throws Exception {
String mask = parameter.getParameterSegmentationOutputFile().getMask();
String oldMethod = parameter.getParameterClustering().getMethodAsString();
double oldThreshold = parameter.getParameterClustering().getThreshold();
String oldModelKind = parameter.getParameterModel().getModelKindAsString();
int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents();
parameter.getParameterModel().setModelKind("FULL");
parameter.getParameterModel().setNumberOfComponents(1);
parameter.getParameterClustering().setMethod("l");
parameter.getParameterClustering().setThreshold(threshold);
ClusterSet clustersLClust = MClust.make(featureSet, clusterSet, parameter, null);
if (parameter.getParameterDiarization().isSaveAllStep()) {
parameter.getParameterSegmentationOutputFile().setMask(mask + ".l.seg");
MainTools.writeClusterSet(parameter, clustersLClust, false);
}
parameter.getParameterSegmentation().setMethod(oldMethod);
parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent);
parameter.getParameterModel().setModelKind(oldModelKind);
parameter.getParameterClustering().setThreshold(oldThreshold);
parameter.getParameterSegmentationOutputFile().setMask(mask);
return clustersLClust;
}
/**
* Speaker clustering.
*
* @param threshold the threshold
* @param method the method
* @param clusterSetBase the cluster set base
* @param clustersSet the clusters set
* @param featureSet the feature set
* @param parameter the parameter
* @return the cluster set
* @throws Exception the exception
*/
public ClusterSet speakerClustering(
double threshold,
String method,
ClusterSet clusterSetBase,
ClusterSet clustersSet,
AudioFeatureSet featureSet,
Parameter parameter)
throws Exception {
String mask = parameter.getParameterSegmentationOutputFile().getMask();
String oldMethod = parameter.getParameterClustering().getMethodAsString();
double oldThreshold = parameter.getParameterClustering().getThreshold();
String oldEMControl = parameter.getParameterEM().getEMControl();
int oldNTop = parameter.getParameterTopGaussian().getScoreNTop();
String oldSpeechDetectorMethod = parameter.getParameterInputFeature().getSpeechMethodAsString();
double oldSpeechDetectorThreshold = parameter.getParameterInputFeature().getSpeechThreshold();
// ** bottom up hierarchical classification using GMMs
// ** one for each cluster, trained by MAP adaptation of a UBM composed of the fusion of
// 4x128GMM
// ** the feature normalization use feature mapping technique, after the cluster frames are
// centered and reduced
String dir = "ester2";
InputStream ubmInputStream = getClass().getResourceAsStream(dir + "/ubm.gmm");
GMMArrayList ubmVect =
MainTools.readGMMContainer(ubmInputStream, parameter.getParameterModel());
GMM ubm = ubmVect.get(0);
// int nbCep = ubm.getDimension() + 1;
String FeatureFormat = "featureSetTransformation";
parameter.getParameterInputFeature().setSpeechMethod("E");
parameter.getParameterInputFeature().setSpeechThreshold(0.1);
AudioFeatureSet featureSet2 =
loadFeature(
featureSet, parameter, clustersSet, FeatureFormat + ",1:3:2:0:0:0,13,1:1:300:4");
parameter.getParameterClustering().setMethod(method);
parameter.getParameterClustering().setThreshold(threshold);
parameter.getParameterEM().setEMControl("1,5,0.01");
parameter.getParameterTopGaussian().setScoreNTop(5);
// ---- Begin NEW v 1.13 ---
// if (parameter.parameterSpeechDetector.useSpeechDetection() == true) {
// MSpeechDetector.EnergyThresholdMethod(clustersSet, featureSet, parameter);
// }
// ---- End NEW v 1.13 ---
boolean saveAll = parameter.getParameterDiarization().isSaveAllStep();
parameter.getParameterDiarization().setSaveAllStep(false);
ClusterSet clustersCLR = MClust.make(featureSet2, clustersSet, parameter, ubm);
parameter.getParameterDiarization().setSaveAllStep(saveAll);
parameter.getParameterSegmentationOutputFile().setMask(mask);
if (parameter.getParameterDiarization().isSaveAllStep()) {
parameter.getParameterSegmentationOutputFile().setMask(mask + ".c.seg");
MainTools.writeClusterSet(parameter, clustersCLR, false);
}
parameter.getParameterSegmentationOutputFile().setMask(mask);
parameter.getParameterClustering().setMethod(oldMethod);
parameter.getParameterClustering().setThreshold(oldThreshold);
parameter.getParameterEM().setEMControl(oldEMControl);
parameter.getParameterTopGaussian().setScoreNTop(oldNTop);
parameter.getParameterInputFeature().setSpeechMethod(oldSpeechDetectorMethod);
parameter.getParameterInputFeature().setSpeechThreshold(oldSpeechDetectorThreshold);
return clustersCLR;
}