Tools implemented in bob.bio.gmm

Summary

bob.bio.gmm.algorithm.GMM(number_of_gaussians)	Algorithm for computing Universal Background Models and Gaussian Mixture Models of the features.
bob.bio.gmm.algorithm.ISV(…[, …])	Tool for computing Unified Background Models and Gaussian Mixture Models of the features
bob.bio.gmm.algorithm.JFA(…[, …])	Tool for computing Unified Background Models and Gaussian Mixture Models of the features and project it via JFA
bob.bio.gmm.algorithm.IVector(…[, …])	Tool for extracting I-Vectors

Details

class bob.bio.gmm.algorithm.GMM(number_of_gaussians, kmeans_training_iterations=25, gmm_training_iterations=25, training_threshold=0.0005, variance_threshold=0.0005, update_weights=True, update_means=True, update_variances=True, relevance_factor=4, gmm_enroll_iterations=1, responsibility_threshold=0, INIT_SEED=5489, scoring_function=<built-in function linear_scoring>)

Bases: bob.bio.base.algorithm.Algorithm

Algorithm for computing Universal Background Models and Gaussian Mixture Models of the features. Features must be normalized to zero mean and unit standard deviation.

enroll(feature_arrays)

Enrolls a GMM using MAP adaptation, given a list of 2D numpy.ndarray’s of feature vectors

enroll_gmm(array)

load_projector(projector_file)

Reads the UBM model from file

load_ubm(ubm_file)

project(feature)

Computes GMM statistics against a UBM, given an input 2D numpy.ndarray of feature vectors

project_ubm(array)

read_feature(feature_file)

Read the type of features that we require, namely GMM_Stats

read_gmm_stats(gmm_stats_file)

Reads GMM stats from file.

read_model(model_file)

Reads the model, which is a GMM machine

save_ubm(projector_file)

Save projector to file

score(model, probe)

Computes the score for the given model and the given probe using the scoring function from the config file

score_for_multiple_probes(model, probes)

This function computes the score between the given model and several given probe files.

train_projector(train_features, projector_file)

Computes the Universal Background Model from the training (“world”) data

train_ubm(array)

class bob.bio.gmm.algorithm.GMMRegular(**kwargs)

Bases: bob.bio.gmm.algorithm.GMM

Algorithm for computing Universal Background Models and Gaussian Mixture Models of the features

load_enroller(enroller_file)

Reads the UBM model from file

score(model, probe)

Computes the score for the given model and the given probe. The score are Log-Likelihood. Therefore, the log of the likelihood ratio is obtained by computing the following difference.

score_for_multiple_probes(model, probes)

train_enroller(train_features, enroller_file)

Computes the Universal Background Model from the training (“world”) data

class bob.bio.gmm.algorithm.ISV(subspace_dimension_of_u, isv_training_iterations=10, isv_enroll_iterations=1, multiple_probe_scoring=None, **kwargs)

Bases: bob.bio.gmm.algorithm.GMM

Tool for computing Unified Background Models and Gaussian Mixture Models of the features

enroll(enroll_features)

Performs ISV enrollment

load_isv(isv_file)

load_projector(projector_file)

Load the GMM and the ISV model from the same HDF5 file

project(feature)

Computes GMM statistics against a UBM, then corresponding Ux vector

project_isv(projected_ubm)

read_feature(feature_file)

Read the type of features that we require, namely GMMStats

read_model(model_file)

Reads the ISV Machine that holds the model

save_projector(projector_file)

Save the GMM and the ISV model in the same HDF5 file

score(model, probe)

Computes the score for the given model and the given probe.

score_for_multiple_probes(model, probes)

This function computes the score between the given model and several given probe files.

train_isv(data)

Train the ISV model given a dataset

train_projector(train_features, projector_file)

Train Projector and Enroller at the same time

write_feature(data, feature_file)

class bob.bio.gmm.algorithm.IVector(subspace_dimension_of_t, tv_training_iterations=25, update_sigma=True, use_whitening=True, use_lda=False, use_wccn=False, use_plda=False, lda_dim=None, lda_strip_to_rank=True, plda_dim_F=50, plda_dim_G=50, plda_training_iterations=50, **kwargs)

Bases: bob.bio.gmm.algorithm.GMM

Tool for extracting I-Vectors

enroll(enroll_features)

Performs IVector enrollment

load_lda(lda_file)

load_plda(plda_file)

load_projector(projector_file)

Load the GMM and the ISV model from the same HDF5 file

load_tv(tv_file)

load_wccn(wccn_file)

load_whitener(whitening_file)

project(feature_array)

Computes GMM statistics against a UBM, then corresponding Ux vector

project_ivector(gmm_stats)

project_lda(ivector)

project_wccn(ivector)

project_whitening(ivector)

read_feature(feature_file)

Read the type of features that we require, namely i-vectors (stored as simple numpy arrays)

read_model(model_file)

Reads the whitened i-vector that holds the model

save_projector(projector_file)

score(model, probe)

Computes the score for the given model and the given probe.

score_for_multiple_probes(model, probes)

This function computes the score between the given model and several given probe files.

train_ivector(training_stats)

train_lda(training_features)

train_plda(training_features)

train_projector(train_features, projector_file)

Train Projector and Enroller at the same time

train_wccn(training_features)

train_whitener(training_features)

write_feature(data, feature_file)

Saves the feature, which is the (whitened) I-Vector.

class bob.bio.gmm.algorithm.JFA(subspace_dimension_of_u, subspace_dimension_of_v, jfa_training_iterations=10, jfa_enroll_iterations=1, **kwargs)

Bases: bob.bio.gmm.algorithm.GMM

Tool for computing Unified Background Models and Gaussian Mixture Models of the features and project it via JFA

enroll(enroll_features)

Enrolls a GMM using MAP adaptation

load_enroller(enroller_file)

Reads the JFA base from file

load_projector(projector_file)

Reads the UBM model from file

read_feature(feature_file)

Reads the projected feature to be enrolled as a model

read_model(model_file)

Reads the JFA Machine that holds the model

score(model, probe)

Computes the score for the given model and the given probe

score_for_multiple_probes(model, probes)

This function computes the score between the given model and several probes.

train_enroller(train_features, enroller_file)