ref: 04281508709213d63fb61446b22fa418bb056975
dir: /python/demos/demo_pitch.py/
#! /usr/bin/env python import sys from aubio import source, pitch, freqtomidi if len(sys.argv) < 2: print "Usage: %s <filename> [samplerate]" % sys.argv[0] sys.exit(1) filename = sys.argv[1] downsample = 1 samplerate = 44100 / downsample if len( sys.argv ) > 2: samplerate = int(sys.argv[2]) win_s = 4096 / downsample # fft size hop_s = 512 / downsample # hop size s = source(filename, samplerate, hop_s) samplerate = s.samplerate tolerance = 0.8 pitch_o = pitch("yin", win_s, hop_s, samplerate) pitch_o.set_unit("freq") pitch_o.set_tolerance(tolerance) pitches = [] confidences = [] # total number of frames read total_frames = 0 while True: samples, read = s() pitch = pitch_o(samples)[0] #pitch = int(round(pitch)) confidence = pitch_o.get_confidence() #if confidence < 0.8: pitch = 0. print "%f %f %f" % (total_frames / float(samplerate), pitch, confidence) pitches += [pitch] confidences += [confidence] total_frames += read if read < hop_s: break if 0: sys.exit(0) #print pitches from numpy import array, ma import matplotlib.pyplot as plt from demo_waveform_plot import get_waveform_plot, set_xlabels_sample2time skip = 1 pitches = array(pitches[skip:]) confidences = array(confidences[skip:]) times = [t * hop_s for t in range(len(pitches))] fig = plt.figure() ax1 = fig.add_subplot(311) ax1 = get_waveform_plot(filename, samplerate = samplerate, block_size = hop_s, ax = ax1) ax1.set_xticklabels([]) def array_from_text_file(filename, dtype = 'float'): import os.path from numpy import array filename = os.path.join(os.path.dirname(__file__), filename) return array([line.split() for line in open(filename).readlines()], dtype = dtype) ax2 = fig.add_subplot(312, sharex = ax1) import sys, os.path ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected' if os.path.isfile(ground_truth): ground_truth = array_from_text_file(ground_truth) true_freqs = ground_truth[:,2] true_freqs = ma.masked_where(true_freqs < 2, true_freqs) true_times = float(samplerate) * ground_truth[:,0] ax2.plot(true_times, true_freqs, 'r') ax2.axis( ymin = 0.9 * true_freqs.min(), ymax = 1.1 * true_freqs.max() ) # plot raw pitches ax2.plot(times, pitches, '--g') # plot cleaned up pitches cleaned_pitches = pitches #cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches) #cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches) cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches) ax2.plot(times, cleaned_pitches, '.-') #ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() ) #ax2.axis( ymin = 55, ymax = 70 ) ax2.set_xticklabels([]) # plot confidence ax3 = fig.add_subplot(313, sharex = ax1) # plot the confidence ax3.plot(times, confidences) # draw a line at tolerance ax3.plot(times, [tolerance]*len(confidences)) ax3.axis( xmin = times[0], xmax = times[-1]) plt.show() set_xlabels_sample2time(ax3, times[-1], samplerate) #plt.savefig(os.path.basename(filename) + '.png', dpi=200)