class StationaryLoudnessIterator():
def __init__(self, model, outputName, loudnessLevelFunction=None):
self.outputName = outputName
self.extractor = StationaryLoudnessExtractor(model, outputName)
self.converged = False
if loudnessLevelFunction is None:
self.loudnessLevelFunction = asIs
else:
self.loudnessLevelFunction = loudnessLevelFunction
def extractLoudness(self, frequencies, intensityLevels, gainInDecibels=0):
self.extractor.process(frequencies, intensityLevels + gainInDecibels)
loudness = self.extractor.outputDict[self.outputName]
loudnessLevel = self.loudnessLevelFunction(loudness)
return loudnessLevel
def process(self,
frequencies,
intensityLevels,
targetLoudnessFrequencies,
targetLoudnessIntensityLevels,
tol,
nIters=5,
alpha=1.0):
if type(targetLoudnessIntensityLevels) is np.ndarray:
targetLoudness = self.extractLoudness(
targetLoudnessFrequencies, targetLoudnessIntensityLevels)
else:
targetLoudness = targetLoudnessIntensityLevels
storedGain = 0
self.converged = False
for i in range(nIters):
loudnessLevel = self.extractLoudness(frequencies, intensityLevels,
storedGain)
error = targetLoudness - loudnessLevel
print(('Gain: %0.3f, Loudness Level: %0.3f, ' + 'Error: %0.3f') %
(storedGain, loudnessLevel, error))
if np.abs(error) < tol:
self.converged = True
break
else:
storedGain += error * alpha
if (i == (nIters - 1)):
print "Reached iteration limit, not solved" \
"within desired error tolerance."
return storedGain
def __init__(self, model, outputName, loudnessLevelFunction=None):
self.outputName = outputName
self.extractor = StationaryLoudnessExtractor(model, outputName)
self.converged = False
if loudnessLevelFunction is None:
self.loudnessLevelFunction = asIs
else:
self.loudnessLevelFunction = loudnessLevelFunction
class StationaryLoudnessIterator():
def __init__(self, model, outputName, loudnessLevelFunction = None):
self.outputName = outputName
self.extractor = StationaryLoudnessExtractor(model, outputName)
self.converged = False
if loudnessLevelFunction is None:
self.loudnessLevelFunction = asIs
else:
self.loudnessLevelFunction = loudnessLevelFunction
def extractLoudness(self, frequencies, intensityLevels, gainInDecibels = 0):
self.extractor.process(frequencies, intensityLevels + gainInDecibels)
loudness = self.extractor.outputDict[self.outputName]
loudnessLevel = self.loudnessLevelFunction(loudness)
return loudnessLevel
def process(self, frequencies, intensityLevels,
targetLoudnessFrequencies,
targetLoudnessIntensityLevels,
tol,
nIters = 5,
alpha = 1.0):
if type(targetLoudnessIntensityLevels) is np.ndarray:
targetLoudness = self.extractLoudness(targetLoudnessFrequencies,
targetLoudnessIntensityLevels)
else:
targetLoudness = targetLoudnessIntensityLevels
storedGain = 0
self.converged = False
for i in range(nIters):
loudnessLevel = self.extractLoudness(frequencies, intensityLevels, storedGain)
error = targetLoudness - loudnessLevel
print (('Gain: %0.3f, Loudness Level: %0.3f, ' +
'Error: %0.3f') % (storedGain, loudnessLevel, error))
if np.abs(error) < tol:
self.converged = True
break
else:
storedGain += error * alpha
if (i == (nIters-1)):
print "Reached iteration limit, not solved within desired error tolerance."
return storedGain
def __init__(self, model, outputName, loudnessLevelFunction = None):
self.outputName = outputName
self.extractor = StationaryLoudnessExtractor(model, outputName)
self.converged = False
if loudnessLevelFunction is None:
self.loudnessLevelFunction = asIs
else:
self.loudnessLevelFunction = loudnessLevelFunction
