+++ /dev/null
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2013 Maxime Le Coz <lecoz@irit.fr>
-
-# This file is part of TimeSide.
-
-# TimeSide is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 2 of the License, or
-# (at your option) any later version.
-
-# TimeSide 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. See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License
-# along with TimeSide. If not, see <http://www.gnu.org/licenses/>.
-
-# Author: Maxime Le Coz <lecoz@irit.fr>
-
-from timeside.core import implements, interfacedoc
-from timeside.analyzer.core import Analyzer
-from timeside.analyzer.utils import melFilterBank, computeModulation
-from timeside.analyzer.utils import segmentFromValues
-from timeside.api import IAnalyzer
-from numpy import array, hamming, dot, mean, float
-from numpy.fft import rfft
-from scipy.signal import firwin, lfilter
-from timeside.analyzer.preprocessors import frames_adapter
-
-
-class IRITSpeech4Hz(Analyzer):
- '''Speech Segmentor based on the 4Hz energy modulation analysis.
-
- Properties:
- - energy4hz (list) : List of the 4Hz energy by frame for the modulation computation
- - threshold (float) : Threshold for the classification Speech/NonSpeech
- - frequency_center (float) : Center of the frequency range where the energy is extracted
- - frequency_width (float) : Width of the frequency range where the energy is extracted
- - orderFilter (int) : Order of the pass-band filter extracting the frequency range
- - normalizeEnergy (boolean) : Whether the energy must be normalized or not
- - nFFT (int) : Number of points for the FFT. Better if 512 <= nFFT <= 2048
- - nbFilters (int) : Length of the Mel Filter bank
- - melFilter (numpy array) : Mel Filter bank
- - modulLen (float) : Length (in second) of the modulation computation window
- '''
-
-<<<<<<< HEAD
- @interfacedoc
-=======
- implements(IAnalyzer)
-
- @interfacedoc
->>>>>>> 7c3ccb1c5b87c4639fee32df595cca1991265657
- def setup(self, channels=None, samplerate=None, blocksize=None,
- totalframes=None):
- super(IRITSpeech4Hz, self).setup(
- channels, samplerate, blocksize, totalframes)
-
- self.energy4hz = []
- # Classification
- self.threshold = 2.0
-
- self.wLen = 1.0
- self.wStep = 0.1
- self.input_blocksize = int(self.wLen * samplerate)
- self.input_stepsize = int(self.wStep * samplerate)
-
- # Pass-band Filter
- self.frequency_center = 4.0
- self.frequency_width = 0.5
- self.orderFilter = 100
-
- self.normalizeEnergy = True
- self.nFFT = 2048
- self.nbFilters = 30
- self.modulLen = 2.0
- self.melFilter = melFilterBank(self.nbFilters, self.nFFT, samplerate)
-
- @staticmethod
- @interfacedoc
- def id():
- return "irit_speech_4hz"
-
- @staticmethod
- @interfacedoc
- def name():
- return "IRIT Speech 4Hz Modulation"
-
- @staticmethod
- @interfacedoc
- def unit():
- return ""
-
- def __str__(self):
- return "Speech confidences indexes"
-
- @frames_adapter
- def process(self, frames, eod=False):
- frames = frames.T[0]
- # windowing of the frame (could be a changeable property)
- w = frames * hamming(len(frames))
-
- # Mel scale spectrum extraction
- f = abs(rfft(w, n=2 * self.nFFT)[0:self.nFFT])
- e = dot(f ** 2, self.melFilter)
-
- self.energy4hz.append(e)
-
- return frames, eod
-
- def post_process(self):
- '''
-
- '''
- # Creation of the pass-band filter
- Wo = self.frequency_center / self.samplerate()
- Wn = [Wo - (self.frequency_width / 2) / self.samplerate(),
- Wo + (self.frequency_width / 2) / self.samplerate()]
- num = firwin(self.orderFilter, Wn, pass_zero=False)
-
- # Energy on the frequency range
- self.energy4hz = array(self.energy4hz)
- energy = lfilter(num, 1, self.energy4hz.T, 0)
- energy = sum(energy)
-
- # Normalization
- if self.normalizeEnergy:
- energy = energy / mean(energy)
-
- # Energy Modulation
- frameLenModulation = int(
- self.modulLen * self.samplerate() / self.input_blocksize)
- modEnergyValue = computeModulation(energy, frameLenModulation, True)
-
- # Confidence Index
- conf = array(modEnergyValue - self.threshold) / self.threshold
- conf[conf > 1] = 1
-
- modEnergy = self.new_result(data_mode='value', time_mode='framewise')
- modEnergy.id_metadata.id += '.' + 'energy_confidence'
- modEnergy.id_metadata.name += ' ' + 'Energy Confidence'
-
- modEnergy.data_object.value = conf
-
- self.process_pipe.results.add(modEnergy)
-
- # Segment
- convert = {False: 0, True: 1}
- label = {0: 'nonSpeech', 1: 'Speech'}
-
- segList = segmentFromValues(modEnergyValue > self.threshold)
- # Hint : Median filtering could imrove smoothness of the result
- # from scipy.signal import medfilt
- # segList = segmentFromValues(medfilt(modEnergyValue > self.threshold, 31))
-
- segs = self.new_result(data_mode='label', time_mode='segment')
- segs.id_metadata.id += '.' + 'segments'
- segs.id_metadata.name += ' ' + 'Segments'
-
- segs.label_metadata.label = label
-
- segs.data_object.label = [convert[s[2]] for s in segList]
- segs.data_object.time = [(float(s[0]) * self.input_blocksize /
- self.samplerate())
- for s in segList]
-<<<<<<< HEAD
- segs.data_object.duration = [(float(s[1]-s[0]) * self.input_blocksize /
-=======
- segs.data_object.duration = [(float(s[1]-s[0]+1) * self.blocksize() /
->>>>>>> 7c3ccb1c5b87c4639fee32df595cca1991265657
- self.samplerate())
- for s in segList]
-
- self.process_pipe.results.add(segs)
-
- return
+++ /dev/null
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2013 Maxime Le Coz <lecoz@irit.fr>
-
-# This file is part of TimeSide.
-
-# TimeSide is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 2 of the License, or
-# (at your option) any later version.
-
-# TimeSide 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. See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License
-# along with TimeSide. If not, see <http://www.gnu.org/licenses/>.
-
-# Author: Maxime Le Coz <lecoz@irit.fr>
-
-from timeside.core import implements, interfacedoc
-from timeside.analyzer.core import Analyzer
-from timeside.analyzer.utils import entropy, computeModulation
-from timeside.analyzer.utils import segmentFromValues
-from timeside.api import IAnalyzer
-from numpy import array
-from scipy.ndimage.morphology import binary_opening
-from timeside.analyzer.preprocessors import frames_adapter
-
-
-class IRITSpeechEntropy(Analyzer):
- """Speech Segmentor based on Entropy analysis."""
-
- implements(IAnalyzer)
-
- @interfacedoc
- def setup(self, channels=None, samplerate=None, blocksize=None,
- totalframes=None):
- super(IRITSpeechEntropy, self).setup(
- channels, samplerate, blocksize, totalframes)
- self.entropyValue = []
- self.threshold = 0.4
- self.smoothLen = 5
- self.modulLen = 2
- self.wLen = 1.0
- self.wStep = 0.1
- self.input_blocksize = int(self.wLen * samplerate)
- self.input_stepsize = int(self.wStep * samplerate)
-
- @staticmethod
- @interfacedoc
- def id():
- return "irit_speech_entropy"
-
- @staticmethod
- @interfacedoc
- def name():
- return "IRIT Speech entropy"
-
- @staticmethod
- @interfacedoc
- def unit():
- return ""
-
- def __str__(self):
- return "Speech confidences indexes"
-
- @frames_adapter
- def process(self, frames, eod=False):
- self.entropyValue.append(entropy(frames))
- return frames, eod
-
- def post_process(self):
- entropyValue = array(self.entropyValue)
- w = self.modulLen * self.samplerate() / self.input_blocksize
- modulentropy = computeModulation(entropyValue, w, False)
- confEntropy = array(modulentropy - self.threshold) / self.threshold
- confEntropy[confEntropy > 1] = 1
-
- conf = self.new_result(data_mode='value', time_mode='framewise')
-
- conf.id_metadata.id += '.' + 'confidence'
- conf.id_metadata.name += ' ' + 'Confidence'
-
- conf.data_object.value = confEntropy
- self.process_pipe.results.add(conf)
-
- # Binary Entropy
- binaryEntropy = modulentropy > self.threshold
- binaryEntropy = binary_opening(
- binaryEntropy, [1] * (self.smoothLen * 2))
-
- convert = {False: 0, True: 1}
- label = {0: 'NonSpeech', 1: 'Speech'}
- segList = segmentFromValues(binaryEntropy)
-
- segs = self.new_result(data_mode='label', time_mode='segment')
- segs.id_metadata.id += '.' + 'segments'
- segs.id_metadata.name += ' ' + 'Segments'
-
- segs.label_metadata.label = label
-
- segs.data_object.label = [convert[s[2]] for s in segList]
- segs.data_object.time = [(float(s[0]) * self.input_blocksize /
- self.samplerate())
- for s in segList]
-<<<<<<< HEAD
- segs.data_object.duration = [(float(s[1]-s[0]) * self.input_blocksize /
-=======
- segs.data_object.duration = [(float(s[1]-s[0]+1) * self.blocksize() /
->>>>>>> 7c3ccb1c5b87c4639fee32df595cca1991265657
- self.samplerate())
- for s in segList]
-
- self.process_pipe.results.add(segs)
-
- return
projects / timeside.git / commitdiff