From: Thomas Fillon <thomas@parisson.com>
Date: Tue, 21 Oct 2014 18:38:27 +0000 (+0200)
Subject: fix numpy related bug and minor updates in irit_speech_4hz analyzer
X-Git-Tag: 0.6~4^2~7
X-Git-Url: https://git.parisson.com/?a=commitdiff_plain;h=bd20eeb726798f92967ba93d8b64eb9e420b6bff;p=timeside.git

fix numpy related bug and minor updates in irit_speech_4hz analyzer
---

diff --git a/timeside/analyzer/irit_speech_4hz.py b/timeside/analyzer/irit_speech_4hz.py
index ecd04d0..dd24bf2 100644
--- a/timeside/analyzer/irit_speech_4hz.py
+++ b/timeside/analyzer/irit_speech_4hz.py
@@ -24,12 +24,12 @@ 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, mod
+import numpy as np
 from numpy.fft import rfft
 from scipy.signal import firwin, lfilter
 from timeside.analyzer.preprocessors import frames_adapter
 
-from ..tools.parameters import Float, HasTraits
+from timeside.tools.parameters import Float, HasTraits
 
 
 class IRITSpeech4Hz(Analyzer):
@@ -114,11 +114,11 @@ class IRITSpeech4Hz(Analyzer):
 
         frames = frames.T[0]
         # windowing of the frame (could be a changeable property)
-        w = frames * hamming(len(frames))
+        w = frames * np.hamming(len(frames))
 
         # Mel scale spectrum extraction
         f = abs(rfft(w, n=2 * self.nFFT)[0:self.nFFT])
-        e = dot(f ** 2, self.melFilter)
+        e = np.dot(f ** 2, self.melFilter)
 
         self.energy4hz.append(e)
 
@@ -135,13 +135,13 @@ class IRITSpeech4Hz(Analyzer):
         num = firwin(self.orderFilter, Wn, pass_zero=False)
 
         # Energy on the frequency range
-        self.energy4hz = array(self.energy4hz)
+        self.energy4hz = np.array(self.energy4hz)
         energy = lfilter(num, 1, self.energy4hz.T, 0)
         energy = sum(energy)
 
         # Normalization
         if self.normalizeEnergy and energy.any():
-            energy = energy / mean(energy)
+            energy = energy / np.mean(energy)
 
         # Energy Modulation
         frameLenModulation = int(
@@ -149,7 +149,7 @@ class IRITSpeech4Hz(Analyzer):
         modEnergyValue = computeModulation(energy, frameLenModulation, True)
 
         # Confidence Index
-        conf = array(modEnergyValue - self.threshold) / self.threshold
+        conf = np.array(modEnergyValue - self.threshold) / self.threshold
         conf[conf > 1] = 1
 
         modEnergy = self.new_result(data_mode='value', time_mode='framewise')
@@ -167,11 +167,11 @@ class IRITSpeech4Hz(Analyzer):
         decision = modEnergyValue > self.threshold
 
         segList = segmentFromValues(decision)
-        # Hint : Median filtering could imrove smoothness of the result
+        # Hint : Median filtering could improve smoothness of the result
         from scipy.signal import medfilt
-        output_samplerate = float(self.samplerate()) / self.input_stepsize
-        N = self.medfilt_duration * output_samplerate
-        N += 1 - mod(N, 2)  # Make N odd
+        output_samplerate = np.float(self.samplerate()) / self.input_stepsize
+        N = int(np.ceil(self.medfilt_duration * output_samplerate))
+        N += 1 - np.mod(N, 2)  # Make N odd
         segList_filt = segmentFromValues(medfilt(decision, N))
 
         segs = self.new_result(data_mode='label', time_mode='segment')
@@ -181,10 +181,10 @@ class IRITSpeech4Hz(Analyzer):
         segs.data_object.label_metadata.label = label
 
         segs.data_object.label = [convert[s[2]] for s in segList]
-        segs.data_object.time = [(float(s[0]) * self.blocksize() /
+        segs.data_object.time = [(np.float(s[0]) * self.blocksize() /
                                  self.samplerate())
                                  for s in segList]
-        segs.data_object.duration = [(float(s[1] - s[0] + 1) * self.blocksize() /
+        segs.data_object.duration = [(np.float(s[1] - s[0] + 1) * self.blocksize() /
                                      self.samplerate())
                                      for s in segList]
 
@@ -198,10 +198,10 @@ class IRITSpeech4Hz(Analyzer):
         segs.data_object.label_metadata.label = label
 
         segs.data_object.label = [convert[s[2]] for s in segList_filt]
-        segs.data_object.time = [(float(s[0]) * self.blocksize() /
+        segs.data_object.time = [(np.float(s[0]) * self.blocksize() /
                                  self.samplerate())
                                  for s in segList_filt]
-        segs.data_object.duration = [(float(s[1] - s[0] + 1) * self.blocksize() /
+        segs.data_object.duration = [(np.float(s[1] - s[0] + 1) * self.blocksize() /
                                      self.samplerate())
                                      for s in segList_filt]