BEAT - pkorshunov/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

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

Parameters 6

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

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import numpy
import bob.ap

class Algorithm:

def __init__(self):
        self.win_length_ms = 20
        self.win_shift_ms = 10
        self.rate = 16000
        self.pre_emphasis_coef = 1.0
        self.mel_scale = True
        self.n_filters = 40

def setup(self, parameters):
        self.rate = float(parameters.get('rate', self.rate))
        self.win_length_ms = float(parameters.get('win_length_ms', self.win_length_ms))
        self.win_shift_ms = float(parameters.get('win_shift_ms', self.win_shift_ms))

self.pre_emphasis_coef = float(parameters.get('pre_emphasis_coef', self.pre_emphasis_coef))
        self.mel_scale = parameters.get('mel_scale', self.mel_scale)
        self.n_filters = parameters.get('n_filters', self.n_filters)
        return True

def process(self, inputs, outputs):
        data = inputs["speech"].data.value.astype('float64')
        vad_labels = inputs["labels"].data.value

# first, trim out the silences from both ends
        # if VAD detection worked on this sample
        if vad_labels.size == 2 and not vad_labels.all():
            # we probably could not read the sample, so no labels were computed
            print('VAD labels for the sample is invalid!')
        else:
            # take only speech frames, since in VAD speech frames are 1 and silence are 0
            speech, = numpy.nonzero(vad_labels)
            if len(speech) < len(vad_labels) and len(speech)>0:  # trim only if necessary
                nzstart=speech[0]*int(self.rate/1000*self.win_shift_ms)  # index of the first non-silent frame
                # make sure we count the length of non-speech shift plus the length of the last frame
                nzend=(speech[-1])*int(self.rate/1000*self.win_shift_ms) + int(self.rate/1000*self.win_length_ms)

data = data[nzstart:nzend]

print("Length of trimmed sample is %s", str(data.shape))

# then, compute the spectrogram
        c = bob.ap.Spectrogram(float(self.rate), float(self.win_length_ms), float(self.win_shift_ms), int(self.n_filters), 0.0, float(self.rate/2.0), float(self.pre_emphasis_coef), bool(self.mel_scale))
        # energy power spectrum
        c.energy_filter = True  # ^2 of FFT spectrum
        # we take no log
        c.log_filter = False
        c.energy_bands = True  # band filtering

spectrogram = c(data)

outputs["data"].write({
            'value':spectrogram
        })
        return True

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

Attestation:

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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 trimmed_spectrogram 2

Endpoint Groups 1

Group: main

Parameters 6

Experiments

algorithms

pkorshunov

trimmed_spectrogram

2