BEAT - elie_khoury/gmm

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

Endpoint Groups 2

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: main

Endpoint Name	Data Format	Nature
features	system/array_2d_floats/1	Input
id	system/text/1	Input
model	tutorial/gmm/1	Output

Unnamed group

Endpoint Name	Data Format	Nature
ubm	tutorial/gmm/1	Input

import bob
import numpy
from bob.machine import GMMMachine

def gmm_from_data(data):
    """Unmangles a bob.machine.GMMMachine from a BEAT Data object"""

dim_c, dim_d = data.means.shape
    gmm = GMMMachine(dim_c, dim_d)
    gmm.weights = data.weights
    gmm.means = data.means
    gmm.variances = data.variances
    gmm.variance_thresholds = data.variance_thresholds
    return gmm

class Algorithm:

def __init__(self):
        self.ubm      = None
        self.features = []

def process(self, inputs, outputs):

# retrieve the UBM once
        if self.ubm is None:
            inputs['ubm'].next()
            self.ubm = gmm_from_data(inputs['ubm'].data)

# collect all the features for the current template
        self.features.append(inputs["features"].data.value)

# adapts the UBM GMM for the template (when all the features have been collected)
        if inputs["id"].isDataUnitDone():
            model = bob.machine.GMMMachine(self.ubm)

trainer = bob.trainer.MAP_GMMTrainer(4)
            trainer.max_iterations = 1
            trainer.set_prior_gmm(self.ubm)
            trainer.train(model, numpy.vstack(self.features))

# outputs data
            outputs["model"].write({
                'weights':              model.weights,
                'means':                model.means,
                'variances':            model.variances,
                'variance_thresholds':  model.variance_thresholds,
            })

self.features = []

return True

The code for this algorithm in Python
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For a given set of feature vectors and a Gaussian Mixture Models (GMM), this algorithm implements the Maximum-a-posteriori (MAP) estimation (adapting only the means).

Details of MAP estimation can be found in the paper: Reynolds, Douglas A., Thomas F. Quatieri, and Robert B. Dunn. "Speaker verification using adapted Gaussian mixture models." Digital signal processing 10.1 (2000): 19-41. A very good description on how the MAP estimation works can be found in the Mathematical Monks's YouTube channel.z

This algorithm relies on the Bob library.

No experiments are using this algorithm.

This algorithm was never executed.

algorithms elie_khoury gmm_projection 2

Endpoint Groups 2

Group: main

Unnamed group

algorithms

elie_khoury

gmm_projection

2