ref: cf31ff1ab7505063cf41b960c3cd56d602839082
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()