By continuing to browse this website, you implicitly agree with our Legal Disclaimer

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

The ruler at 80 columns indicate suggested POSIX line breaks (for readability).

The editor will automatically enlarge to accomodate the entirety of your input

Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

For a Gaussian Mixture Models (GMM), this algorithm implements the Universal Background Model (UBM) training described in [Reynolds2000].

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 (ML) estimator.

This algorithm relies on the Bob library.

The input, features, is a training set of floating point vectors as a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of training samples, and the number of columns to the dimensionality of the training samples. The output, ubm, is the GMM trained using the ML estimator.

[Reynolds2000] | 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. |

This table shows the number of times this algorithm
has been **successfully** run using the given environment. Note
this does not provide sufficient information to evaluate if the
algorithm will run when submitted to different conditions.

Terms of Service | Contact Information | BEAT platform version 1.5.4b0 | © Idiap Research Institute - 2013-2020