I'm a musician and I'm making a script that takes a wave file and snaps each of its frequencies from the fourier transforms to the nearest musical harmonic. Thanks to help from another question I posted here, that part works, but what I need to do now is to experiment with some interesting ways to interpolate between the before and after version. I don't want to just do a crossfade because I want it to sound like one sound continuously morphing rather than one fading out and in. One idea I have is to snap one frequency at a time, maybe either in order based on the volume of that frequency by the end, from the lowest frequencies to the highest, highest to lowest, or random. Another idea was to interpolate them like image interpolation where if I have a frequency at 100hz with a volume of 255 and another at 120hz with a volume of 215, halfway between the interpolation I should have a frequency at 110hz with a volume of 235. If anyone knows of a function library that lets me do these then please let me know, otherwise I'll be making them and will need some help on getting started. How would I accomplish either of these?
Another thought I had was to reduce the number of harmonics over time so that in the beginning there are enough harmonics for the sound to be indistinguishable from the original but by the end there are only a couple. To get what I mean by the number of harmonics see the part of the script below where I calculate valid_ratios and valid_frequencies.
This is the sample I'm testing with (rename it to missile.wav): https://my.mixtape.moe/iltlos.wav
Here is the script so far:
import numpy as np import scipy.io.wavfile samples, data = scipy.io.wavfile.read("missile.wav") # fast fourier transform makes an array of the frequencies of sine waves that comprise the sound data_fft = np.fft.rfft(data) # the higher MAX_HARMONIC is, the more it sounds like the original, # the lower it is, the more it sounds like an instrument MAX_HARMONIC = 2 # generate list of ratios that can be used for tuning (not octave reduced) valid_ratios =  for i in range(1, MAX_HARMONIC + 1): for j in range(1, MAX_HARMONIC + 1): if i % 2 != 0 and j % 2 != 0: valid_ratios.append(i/float(j)) valid_ratios.append(j/float(i)) # remove dupes valid_ratios = list(set(valid_ratios)) # find all the frequencies with the valid ratios valid_frequencies =  multiple = 2 while(multiple < samples): multiple *= 2 for ratio in valid_ratios: frequency = ratio * multiple if frequency < samples: valid_frequencies.append(frequency) # remove dupes and sort and turn into a numpy array valid_frequencies = np.sort(np.array(list(set(valid_frequencies)))) # bin the data_fft into the nearest valid frequency valid_frequencies = valid_frequencies.astype(np.int64) boundaries = np.concatenate([, np.round(np.sqrt(0.25 + valid_frequencies[:-1] * valid_frequencies[1:])).astype(np.int64)]) select = np.abs(data_fft) > 1 filtered_data_fft = np.zeros_like(data_fft) filtered_data_fft[valid_frequencies] = np.add.reduceat(np.where(select, data_fft, 0), boundaries) # do the inverse fourier transform to get a sound wave back recovered_signal = np.fft.irfft(filtered_data_fft) # amplify to ceiling recovered_signal /= np.amax(recovered_signal) scipy.io.wavfile.write("missile_output.wav", samples, recovered_signal)