Bob 2.0 computation of average ratios between subbands in 2D features.

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
data system/array_2d_floats/1 Input
features system/array_1d_floats/1 Output

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

Name Description Type Default Range/Choices
n_ratios How many ratios to compute (the size of the output features) uint32 10
n_filters The number of filter bands used in spectrogram computation uint32 40

The code for this algorithm in Python
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An audio spectrogram is assumed to be the input feature but it can be any 2d array of floats. The algorithm will split the vertical axis into the specfied number of subbands and computes ratios between the averages (along the horizontal axis) of these subbands.

Experiments

Updated Name Databases/Protocols Analyzers
pkorshunov/pkorshunov/speech-antispoofing-baseline/1/btas2016-baseline-pa avspoof/1@physicalaccess_antispoofing pkorshunov/simple_antispoofing_analyzer/2

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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