Source code for bob.pad.voice.extractor.vectors_ratios

#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Pavel Korshunov <>
# Tue 17 May 15:43:22 CEST 2016

import numpy

from bob.pad.voice.extractor import Ratios
import math

import logging

logger = logging.getLogger("bob.pad.voice")

class VectorsRatios(Ratios):
    def __init__(self,
                 features_processor,  # another extractor that provides features for LBP computation
        self.feature_vector_length = feature_vector_length
        self.vectors_overlap = vectors_overlap

[docs] def compute_ratios(self, data): if len(data) < self.feature_vector_length: logger.error( "- Extraction: the size of features %d is not enough to construct feature vector of this size: %d", len(data), self.feature_vector_length) return numpy.zeros(2) # carefuly compute the end until we loop endloop = len(data) - self.feature_vector_length # find the size of each band (a stip of features, for which we compute ratio) band_length = math.floor(self.features_processor.n_filters / self.n_bands) vector_ratios = [] # we slide the window through the features shifting by the value of vectors_overlap for i in range(0, endloop, self.vectors_overlap): # compute ratio between the highest and the lowest band lower_band = data[i:i + self.feature_vector_length, 0:band_length] higher_band = data[i:i + self.feature_vector_length, -band_length:] ratios = [numpy.mean(lower_band) / numpy.mean(higher_band)] # compute ratio between the rest of the bands for j in range(1, self.n_bands): higher_band = data[i:i + self.feature_vector_length, j * band_length:(j + 1) * band_length] ratios.append(numpy.mean(lower_band) / numpy.mean(higher_band)) lower_band = higher_band vector_ratios.append(ratios) vector_ratios = numpy.asarray(vector_ratios, dtype=numpy.float64) return vector_ratios
def __call__(self, input_data, annotations): """Use VAD to filter out useless energy bands""" if self.features_processor is not None: feature_bands = self.features_processor(input_data, annotations) if feature_bands.any(): ratios = self.compute_ratios(feature_bands)"- Extractions: computed ratios of size: %s ", str(ratios.shape)) return ratios from .spectrogram_extended import SpectrogramExtended extractor = VectorsRatios(features_processor=SpectrogramExtended())