def _calcMinMajRatioImpact(self, minMajRatio, metricalAccentLevel,
triadType):
maxMinMajRatioImpact = self._minMajRationMaxImpact[triadType][metricalAccentLevel]
minMajRatioImpact = linlin(minMajRatio, MINEMOTIONALFEATURES,
MAXEMOTIONALFEATURES, 0,
maxMinMajRatioImpact)
return minMajRatioImpact
def _calcHarmonicComplexityImpactOnProfile(self, harmonicComplexity,
metricalAccentLevel):
harmonicComplexityMaxImpact = self._profileDistanceMaxImpact[
metricalAccentLevel]
harmonicComplxImpactOnProfile = linlin(harmonicComplexity,
MINEMOTIONALFEATURES,
MAXEMOTIONALFEATURES, 0,
harmonicComplexityMaxImpact)
return harmonicComplxImpactOnProfile
def _calcChordNoteScores(self, pitchOptions, metricalAccent,
melodicComplexity):
"""Calculate the score for chord notes, which favours the
fundamental over the 3rd and other components of the chord
Args:
pitchOptions (dict): Available pitch options for a given
backbone note, with info to note type. The dict is of the
type {33: "fundamental", 44: "3rd",...}
metricalAccent (int): Metrical accent of the backbone note
melodicComplexity (float): Music feature
Returns:
scores (dict): Dict of the type {pitch: score, ...}
"""
scores = {}
# iterate through all options and get score for given pitch/metrical
# accent
for pitch, type in pitchOptions.items():
score = self._chordNoteScores[type][metricalAccent]
scores[pitch] = score
maxVal = 0
# get biggest score in chord note scores
for type, values in self._chordNoteScores.items():
v = self._chordNoteScores[type][metricalAccent]
if v > maxVal:
maxVal = v
# calculate middle point in scores
attractionValue = maxVal / 2.0
# calculate attraction rate for compression
melodicComplexityImpact = linlin(
melodicComplexity, MIN, MAX, 0,
self._modifiers["maxMelodicComplexityImpact"])
# compress the scores based on melodic complexity
scores = self._compressValues(attractionValue, scores,
melodicComplexityImpact)
return scores
def _calcMelodicGravityScores(self, pitchOptions, previousPitch,
pitchRange):
"""Calculate the score for melodic gravity, which favours the
closeness between subsequent notes of the backbone
Args:
pitchOptions (list): Available pitch options for a given
backbone note
previousPitch (int): Pitch of previous backbone note
pitchRange (float): Music feature that acts as a modifier
Returns:
scores (dict): Dict of the type {pitch: score, ...}
"""
# calculate distance between previous pitch and pitch options
pitchDistances = [abs(p - previousPitch) for p in pitchOptions]
scores = {}
# iterate through all distances, select relevant score and update
# scores dict
for i, d in enumerate(pitchDistances):
score = self._melGravityScores[d]
pitch = pitchOptions[i]
scores[pitch] = score
maxVal = max(list(self._melGravityScores.values()))
# calculate middle point in scores
attractionValue = maxVal / 2.0
# calculate attraction rate for compression
pitchRangeImpact = linlin(pitchRange, MIN, MAX, 0,
self._modifiers["maxPitchRangeImpact"])
# compress the scores based on pitch range
scores = self._compressValues(attractionValue, scores,
pitchRangeImpact)
return scores
def _calcDissonanceProb(self, harmonicComplexity, metricalAccentLevel):
maxDissonanceProb = self._dissonanceData["probability"][metricalAccentLevel]
dissonanceProb = linlin(harmonicComplexity, MINEMOTIONALFEATURES,
MAXEMOTIONALFEATURES, 0, maxDissonanceProb)
return dissonanceProb