4 from aubio import pitch
8 hop_size = buf_size // 4
14 def sinewave(freq, duration, samplerate = samplerate):
15 """ generate a sinewave """
17 while length < duration * samplerate:
19 return np.sin( 2. * np.pi * np.arange(length) * freq / samplerate ).astype("float32")
21 def get_stats_for_pitch_method(method, freqs, samplerate = samplerate):
22 """ for a given pitch method and a list of frequency, generate a sinewave
23 and get mean deviation """
24 means = np.zeros(len(freqs))
25 medians = np.zeros(len(freqs))
26 for freq, fn in zip(freqs, range(len(freqs))):
27 s = sinewave(freq, .50).reshape(-1, hop_size)
28 #s = (sinewave(freq, .50) + .0*sinewave(freq/2., .50)).reshape(-1, hop_size)
29 p = pitch(method, buf_size, hop_size, samplerate = samplerate)
30 candidates = np.zeros(len(s))
31 #samples = np.zeros(buf_size)
32 for frame, i in zip(s, range(len(s))):
33 candidates[i] = p(frame)[0]
34 # skip first few candidates
35 candidates = candidates[4:]
36 means[fn] = np.mean(candidates[candidates != 0] - freq)
37 medians[fn] = np.median(candidates[candidates != 0] - freq)
38 print (freq, means[fn], medians[fn])
41 if __name__ == '__main__':
42 freqs = np.arange(minfreq, maxfreq, 1.)
43 modes = ["yin", "yinfft"]
45 means, medians = get_stats_for_pitch_method(mode, freqs)
47 plt.plot(freqs, means, 'g-')
48 plt.plot(freqs, medians, 'r--')
49 #plt.savefig(mode + '_deviations_test.png', dpi=300)