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Implements the Universal Background Model (UBM) training

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

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.

Endpoint Name | Data Format | Nature |
---|---|---|

features | system/array_2d_floats/1 | Input |

ubm | tutorial/gmm/1 | Output |

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name | Description | Type | Default | Range/Choices |
---|---|---|---|---|

number-of-gaussians | uint32 | 100 | ||

maximum-number-of-iterations | uint32 | 10 |

The code for this algorithm in Python

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For a Gaussian Mixture Models (GMM), this algorithm implements the Universal Background Model (UBM) training described 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.

First, this algorithm estimates the means, diagonal covariance matrix and the weights of each gaussian component using the KMeans clustering. After only the means are re-estimated using the Maximum Likelihood estimator.

This algorithm relies on the Bob library.

The following parameters are configurable:

- 'number-of-gaussians': The number of Gaussian Components
- 'maximum-number-of-iterations': The maximum number of iterations for the EM algorithm.

No experiments are using this algorithm.

This algorithm was never executed.

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