Bob 2.0 computation of spectrogram for audio samples. The silent tail/head are trimmed.

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
labels system/array_1d_integers/1 Input
speech system/array_1d_floats/1 Input
data system/array_2d_floats/1 Output

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

Name Description Type Default Range/Choices
mel_scale Apply Mel-scale filtering or use linear (default - linear) bool False
pre_emphasis_coef Pre-emphasis coefficient, used in the spectrogram computation float64 1.0
win_shift_ms The length of the overlap between neighboring windows. Typically the half of window length. float64 10.0
win_length_ms The length of the sliding processing window, typically about 20 ms float64 20.0
rate Sampling rate of the speech signal float64 16000.0 [2000.0, 256000.0]
n_filters The number of filter bands used in spectrogram computation uint32 40

The code for this algorithm in Python
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Returns trimmed-spectrogram of an audio sample. Silent start and end of a sample are trimmed using Voice Activity Detection (VAD) labels as input.

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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