/**
* Segmentation.
*
* @param method the method
* @param kind the kind
* @param clusterSet the cluster set
* @param featureSet the feature set
* @param parameter the parameter
* @return the cluster set
* @throws Exception the exception
*/
public ClusterSet segmentation(
String method,
String kind,
ClusterSet clusterSet,
AudioFeatureSet featureSet,
Parameter parameter)
throws Exception {
String mask = parameter.getParameterSegmentationOutputFile().getMask();
String oldMethod = parameter.getParameterSegmentation().getMethodAsString();
int oldNumberOfComponent = parameter.getParameterModel().getNumberOfComponents();
String oldModelKind = parameter.getParameterModel().getModelKindAsString();
parameter.getParameterSegmentation().setMethod(method);
parameter.getParameterModel().setNumberOfComponents(1);
parameter.getParameterModel().setModelKind(kind);
ClusterSet clustersSeg = new ClusterSet();
MSeg.make(featureSet, clusterSet, clustersSeg, parameter);
if (parameter.getParameterDiarization().isSaveAllStep()) {
parameter.getParameterSegmentationOutputFile().setMask(mask + ".s.seg");
MainTools.writeClusterSet(parameter, clustersSeg, false);
}
parameter.getParameterSegmentation().setMethod(oldMethod);
parameter.getParameterModel().setNumberOfComponents(oldNumberOfComponent);
parameter.getParameterModel().setModelKind(oldModelKind);
parameter.getParameterSegmentationOutputFile().setMask(mask);
return clustersSeg;
}
/**
* 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;
}
public ClusterSet make(
AudioFeatureSet featureSet,
ClusterSet clusterSet,
GMMArrayList gmmList,
GMMArrayList gmmTopList,
Parameter parameter)
throws DiarizationException, IOException {
logger.info("Compute Score");
int size = gmmList.size();
logger.finer("GMM size:" + size);
ArrayList<String> genderString = new ArrayList<String>();
ArrayList<String> bandwidthString = new ArrayList<String>();
for (int i = 0; i < size; i++) {
String gmmName = gmmList.get(i).getName();
if (parameter.getParameterScore().isGender() == true) {
if (gmmName.equals("MS")) {
genderString.add(Cluster.genderStrings[1]);
bandwidthString.add(Segment.bandwidthStrings[2]);
} else if (gmmName.equals("FS")) {
genderString.add(Cluster.genderStrings[2]);
bandwidthString.add(Segment.bandwidthStrings[2]);
} else if (gmmName.equals("MT")) {
genderString.add(Cluster.genderStrings[1]);
bandwidthString.add(Segment.bandwidthStrings[1]);
} else if (gmmName.equals("FT")) {
genderString.add(Cluster.genderStrings[2]);
bandwidthString.add(Segment.bandwidthStrings[1]);
} else {
genderString.add(Cluster.genderStrings[0]);
bandwidthString.add(Segment.bandwidthStrings[0]);
}
} else {
genderString.add(Cluster.genderStrings[0]);
bandwidthString.add(Segment.bandwidthStrings[0]);
}
}
ClusterSet clusterSetResult = new ClusterSet();
for (Cluster cluster : clusterSet.clusterSetValue()) {
double[] sumScoreVector = new double[size];
int[] sumLenghtVector = new int[size];
double ubmScore = 0.0;
GMM gmmTop = null;
if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
gmmTop = gmmTopList.get(0);
}
Arrays.fill(sumScoreVector, 0.0);
Arrays.fill(sumLenghtVector, 0);
for (Segment currantSegment : cluster) {
Segment segment = (currantSegment.clone());
int end = segment.getStart() + segment.getLength();
featureSet.setCurrentShow(segment.getShowName());
double[] scoreVector = new double[size];
double maxScore = 0.0;
int idxMaxScore = 0;
for (int i = 0; i < size; i++) {
gmmList.get(i).score_initialize();
}
for (int start = segment.getStart(); start < end; start++) {
for (int i = 0; i < size; i++) {
GMM gmm = gmmList.get(i);
if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
if (i == 0) {
gmmTop.score_getAndAccumulateAndFindTopComponents(
featureSet, start, parameter.getParameterTopGaussian().getScoreNTop());
}
gmm.score_getAndAccumulateForComponentSubset(
featureSet, start, gmmTop.getTopGaussianVector());
} else {
gmm.score_getAndAccumulate(featureSet, start);
}
}
}
if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
ubmScore = gmmTop.score_getMeanLog();
gmmTop.score_getSumLog();
gmmTop.score_getCount();
gmmTop.score_reset();
}
for (int i = 0; i < size; i++) {
GMM gmm = gmmList.get(i);
scoreVector[i] = gmm.score_getMeanLog();
sumLenghtVector[i] += gmm.score_getCount();
sumScoreVector[i] += gmm.score_getSumLog();
if (i == 0) {
maxScore = scoreVector[0];
idxMaxScore = 0;
} else {
double value = scoreVector[i];
if (maxScore < value) {
maxScore = value;
idxMaxScore = i;
}
}
gmm.score_reset();
}
if (parameter.getParameterScore().isTNorm()) {
double sumScore = 0;
double sum2Score = 0;
for (int i = 0; i < size; i++) {
sumScore += scoreVector[i];
sum2Score += (scoreVector[i] * scoreVector[i]);
}
for (int i = 0; i < size; i++) {
double value = scoreVector[i];
double mean = (sumScore - value) / (size - 1);
double et = Math.sqrt(((sum2Score - (value * value)) / (size - 1)) - (mean * mean));
scoreVector[i] = (value - mean) / et;
}
}
if (parameter.getParameterScore().isGender() == true) {
segment.setBandwidth(bandwidthString.get(idxMaxScore));
segment.setInformation("segmentGender", genderString.get(idxMaxScore));
}
if (parameter.getParameterScore().isBySegment()) {
for (int k = 0; k < size; k++) {
double score = scoreVector[k];
GMM gmm = gmmList.get(k);
segment.setInformation("score:" + gmm.getName(), score);
currantSegment.setInformation("score:" + gmm.getName(), score);
}
if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
segment.setInformation("score:" + "UBM", ubmScore);
currantSegment.setInformation("score:" + "UBM", ubmScore);
}
}
String newName = cluster.getName();
if (parameter.getParameterScore().isByCluster() == false) {
if ((scoreVector[idxMaxScore] > parameter.getParameterSegmentation().getThreshold())
&& (parameter.getParameterScore().getLabel()
!= ParameterScore.LabelType.LABEL_TYPE_NONE.ordinal())) {
if (parameter.getParameterScore().getLabel()
== ParameterScore.LabelType.LABEL_TYPE_ADD.ordinal()) {
newName += "_";
newName += gmmList.get(idxMaxScore).getName();
} else {
newName = gmmList.get(idxMaxScore).getName();
}
}
Cluster temporaryCluster = clusterSetResult.getOrCreateANewCluster(newName);
temporaryCluster.setGender(cluster.getGender());
if (parameter.getParameterScore().isGender() == true) {
temporaryCluster.setGender(genderString.get(idxMaxScore));
}
temporaryCluster.addSegment(segment);
}
}
if (parameter.getParameterScore().isByCluster()) {
for (int i = 0; i < size; i++) {
sumScoreVector[i] /= sumLenghtVector[i];
}
if (parameter.getParameterScore().isTNorm()) {
double sumScore = 0;
double sum2Score = 0;
for (int i = 0; i < size; i++) {
sumScore += sumScoreVector[i];
sum2Score += (sumScoreVector[i] * sumScoreVector[i]);
}
for (int i = 0; i < size; i++) {
double value = sumScoreVector[i];
double mean = (sumScore - value) / (size - 1);
double et = Math.sqrt(((sum2Score - (value * value)) / (size - 1)) - (mean * mean));
sumScoreVector[i] = (value - mean) / et;
}
}
double maxScore = sumScoreVector[0];
int idxMaxScore = 0;
for (int i = 1; i < size; i++) {
double s = sumScoreVector[i];
if (maxScore < s) {
maxScore = s;
idxMaxScore = i;
}
}
String newName = cluster.getName();
if ((sumScoreVector[idxMaxScore] > parameter.getParameterSegmentation().getThreshold())
&& (parameter.getParameterScore().getLabel()
!= ParameterScore.LabelType.LABEL_TYPE_NONE.ordinal())) {
if (parameter.getParameterScore().getLabel()
== ParameterScore.LabelType.LABEL_TYPE_ADD.ordinal()) {
newName += "_";
newName += gmmList.get(idxMaxScore).getName();
} else {
newName = gmmList.get(idxMaxScore).getName();
}
// logger.finer("cluster name=" + cluster.getName() + " new_name=" + newName);
}
Cluster tempororaryCluster = clusterSetResult.getOrCreateANewCluster(newName);
tempororaryCluster.setGender(cluster.getGender());
if (parameter.getParameterScore().isGender() == true) {
tempororaryCluster.setGender(genderString.get(idxMaxScore));
}
tempororaryCluster.setName(newName);
for (Segment currantSegment : cluster) {
Segment segment = (currantSegment.clone());
if (parameter.getParameterScore().isGender() == true) {
segment.setBandwidth(bandwidthString.get(idxMaxScore));
}
tempororaryCluster.addSegment(segment);
}
for (int k = 0; k < size; k++) {
double score = sumScoreVector[k];
GMM gmm = gmmList.get(k);
// logger.finer("****clustername = " + newName + " name=" + gmm.getName() + " =" + score+"
// k="+k);
// logger.log(Level.SEVERE, "****clustername = " + newName + " name=" + gmm.getName() + "
// =" + score);
tempororaryCluster.setInformation("score:" + gmm.getName(), score);
ClusterResult cr = new ClusterResult();
cr.setName(newName);
cr.getValue().put(score, gmm.getName());
System.out.println(
"------ clusterResultSet.putValue(newName, gmm.getName(), score)=----------------");
System.out.println(newName + " " + gmm.getName() + " " + score);
if (isName(gmm.getName())) {
clusterResultSet.putValue(newName, gmm.getName(), score);
} else {
System.out.println("*****************" + gmm.getName() + " Non nome valido ");
}
}
if (parameter.getParameterTopGaussian().getScoreNTop() >= 0) {
// tempororaryCluster.putInformation("score:" + "length", ubmSumLen);
// tempororaryCluster.putInformation("score:" + "UBM", ubmSumScore / ubmSumLen);
}
}
}
this.clusterSetResult = clusterSetResult;
return clusterSetResult;
}