Compute the GMM Statistics

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

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
ubm tutorial/gmm/1 Input
features system/array_2d_floats/1 Input
statistics tutorial/gmm_statistics/1 Output

The code for this algorithm in Python
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For a given set of feature vectors and a Gaussian Mixture Model (GMM), this algorithm computes the 0th, 1st and 2nd order GMM Statistics (Baum-Welch) relying on Bob implementation.

This algorithm relies on the Bob library.

Different of the gmm_statistics algorithm, this one works with only one synchronization channel.

The inputs are:

  • features: A set of floating point vectors as a two-dimensional array (64 bits) of a client. The number of rows correspond to the number of samples, and the number of columns to the dimensionality of the samples.
  • ubm: A GMM corresponding to the Universal Background Model.

The output are the statistics of the GMM of a given set of feature vectors (MAP adaptation).

Experiments

Updated Name Databases/Protocols Analyzers
smarcel/tutorial/full_isv/2/mobio_male-gmm_100Gx10I-isv_50Ux10Ix4R-dct_12Bx8Ox45C-seed101 mobio/1@male tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/bancaMc_isv_DCT12x8_100G_U50 banca/1@Mc tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/xm2vtsLp1_isv_DCT12x8_100G_U50 xm2vts/1@lp1 tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/mobioMale_isv_DCT12x8_100G_U50 mobio/1@male tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/bancaP_isv_DCT12x8_100G_U50 banca/1@P tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/atnt_isv_DCT12x8_100G_U50 atnt/1@idiap_test_eyepos tutorial/eerhter_postperf_iso/1

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.

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