1 files changed, 103 insertions, 0 deletions
diff --git a/python/demos/demo_pitch.py b/python/demos/demo_pitch.py
new file mode 100755
index 0000000..555a30e
--- /dev/null
+++ b/python/demos/demo_pitch.py
@@ -0,0 +1,103 @@
+#! /usr/bin/env python
+
+import sys
+from aubio import source, pitch
+
+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("midi")
+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
+import os.path
+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)
+plt.setp(ax1.get_xticklabels(), visible = False)
+ax1.set_xlabel('')
+
+def array_from_text_file(filename, dtype = 'float'):
+    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)
+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 )
+plt.setp(ax2.get_xticklabels(), visible = False)
+ax2.set_ylabel('f0 (midi)')
+
+# 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])
+ax3.set_ylabel('condidence')
+set_xlabels_sample2time(ax3, times[-1], samplerate)
+plt.show()
+#plt.savefig(os.path.basename(filename) + '.svg')