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spectral_tilt.py

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+#!/usr/bin/env python3
+
+import math
+import parselmouth
+import statsmodels.api as sm
+
+from parselmouth.praat import call
+
+voice_id = "test_voices/f4047_ah.wav"
+
+sound= parselmouth.Sound(voice_id)  # read the sound
+window_length_in_millisecs = 32  # allow advanced user to select between 2^5 and 2^12 ms, changing exponent
+window_length = window_length_in_millisecs / 1000
+
+# Compute begin and end times, set window
+end = call(sound, "Get end time")
+midpoint = end / 2
+
+begintime = midpoint - (window_length / 2)
+endtime = midpoint + (window_length / 2)
+part_to_measure = sound.extract_part(begintime , endtime)
+# part_to_measure.save("part_of_sound.wav", "WAV") # option this for advanced users
+spectrum = part_to_measure.to_spectrum()
+total_bins = spectrum.get_number_of_bins()
+dBValue = []
+bins = []
+
+# convert spectral values to dB
+for i in range(2, total_bins):
+    bin_number = (i - 1)
+    currentX = bin_number
+    realValue = spectrum.get_real_value_in_bin(i)
+    imagValue = spectrum.get_imaginary_value_in_bin(i)
+    sumOfSquares = realValue ** 2 + imagValue ** 2
+    rmsPower = sumOfSquares ** 0.5
+    dBValue.append(20 * (math.log10(rmsPower / 0.0002)))
+    bins.append(bin_number)
+
+# find maximum dB value, for rescaling purposes
+maxdB = max(dBValue)
+mindB = min(dBValue)
+rangedB = maxdB - mindB
+
+# stretch the spectrum to a normalized range
+# that matches the number of frequency values
+scalingConstant = ((total_bins - 1) / rangedB)
+dBValue = [(value + abs(mindB))*scalingConstant for value in dBValue]
+
+# find slope of regression
+model = sm.OLS(dBValue,bins)
+results = model.fit()
+spectral_tilt = results.params[0]
+print(spectral_tilt)