Bob 2.0 computation of average ratios between subbands in 2D features.
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 |
|---|---|---|
| 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 |
xxxxxxxxxx################################################################################ ## Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/ ## Contact: beat.support@idiap.ch ## ## This file is part of the beat.core module of the BEAT platform. ## ## Commercial License Usage ## Licensees holding valid commercial BEAT licenses may use this file in ## accordance with the terms contained in a written agreement between you ## and Idiap. For further information contact tto@idiap.ch ## ## Alternatively, this file may be used under the terms of the GNU Affero ## Public License version 3 as published by the Free Software and appearing ## in the file LICENSE.AGPL included in the packaging of this file. ## The BEAT platform is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ## or FITNESS FOR A PARTICULAR PURPOSE. ## ## You should have received a copy of the GNU Affero Public License along ## with the BEAT platform. If not, see http://www.gnu.org/licenses/. ## ################################################################################import numpyimport mathclass Algorithm: def __init__(self): self.n_bands = 40 self.n_ratios = 10 def setup(self, parameters): self.n_bands = parameters.get('n_filters', self.n_bands) self.n_ratios = parameters.get('n_ratios', self.n_ratios) return True def process(self, inputs, outputs): data = inputs["data"].data.value band_length = math.floor(self.n_bands/self.n_ratios) # first, add ratio between the highest and the lowest band lower_band = data[:, 0:band_length] higher_band = data[:, -band_length:] ratios = [numpy.mean(lower_band)/numpy.mean(higher_band)] for i in range(1, self.n_ratios): higher_band = data[:, i*band_length:(i+1)*band_length] ratios.append(numpy.mean(lower_band)/numpy.mean(higher_band)) lower_band = higher_band ratios = numpy.asarray(ratios, dtype=numpy.float64) outputs["features"].write({ 'value':ratios }) return TrueThe 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.
| 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.