ref: ac2652608e25582d843afbf3e7d9a6aaab6d1f8e
dir: /python/demos/demo_bench_yin.py/
#! /usr/bin/env python
import numpy as np
from aubio import pitch
import pylab as plt
buf_size = 2048 * 1
hop_size = buf_size // 4
samplerate = 44100
minfreq = 40
maxfreq = 6000
def sinewave(freq, duration, samplerate = samplerate):
""" generate a sinewave """
length = hop_size
while length < duration * samplerate:
length += hop_size
return np.sin( 2. * np.pi * np.arange(length) * freq / samplerate ).astype("float32")
def get_stats_for_pitch_method(method, freqs, samplerate = samplerate):
""" for a given pitch method and a list of frequency, generate a sinewave
and get mean deviation """
means = np.zeros(len(freqs))
medians = np.zeros(len(freqs))
for freq, fn in zip(freqs, range(len(freqs))):
s = sinewave(freq, .50).reshape(-1, hop_size)
#s = (sinewave(freq, .50) + .0*sinewave(freq/2., .50)).reshape(-1, hop_size)
p = pitch(method, buf_size, hop_size, samplerate = samplerate)
candidates = np.zeros(len(s))
#samples = np.zeros(buf_size)
for frame, i in zip(s, range(len(s))):
candidates[i] = p(frame)[0]
# skip first few candidates
candidates = candidates[4:]
means[fn] = np.mean(candidates[candidates != 0] - freq)
medians[fn] = np.median(candidates[candidates != 0] - freq)
print (freq, means[fn], medians[fn])
return means, medians
if __name__ == '__main__':
freqs = np.arange(minfreq, maxfreq, 1.)
modes = ["yin", "yinfft"]
for mode in modes:
means, medians = get_stats_for_pitch_method(mode, freqs)
plt.figure()
plt.plot(freqs, means, 'g-')
plt.plot(freqs, medians, 'r--')
#plt.savefig(mode + '_deviations_test.png', dpi=300)
plt.show()