BEAT - pkorshunov/band

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

Endpoint Groups 1

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 2

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

###############################################################################
#                                                                             #
# 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 numpy
import math

class 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 True

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

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

Attestation:

Privacy:

Status:

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

Scientific Python 2.7 (0.1.0) 21

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.

algorithms pkorshunov band_ratios 1

Endpoint Groups 1

Group: main

Parameters 2

Experiments

algorithms

pkorshunov

band_ratios

1