1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
|
#! /usr/bin/env python
import sys, os.path
from aubio import pvoc, source, float_type
from numpy import zeros, log10, vstack
import matplotlib.pyplot as plt
def get_spectrogram(filename, samplerate = 0):
win_s = 512 # fft window size
hop_s = win_s // 2 # hop size
fft_s = win_s // 2 + 1 # spectrum bins
a = source(filename, samplerate, hop_s) # source file
if samplerate == 0: samplerate = a.samplerate
pv = pvoc(win_s, hop_s) # phase vocoder
specgram = zeros([0, fft_s], dtype=float_type) # numpy array to store spectrogram
# analysis
while True:
samples, read = a() # read file
specgram = vstack((specgram,pv(samples).norm)) # store new norm vector
if read < a.hop_size: break
# plotting
fig = plt.imshow(log10(specgram.T + .001), origin = 'bottom', aspect = 'auto', cmap=plt.cm.gray_r)
ax = fig.axes
ax.axis([0, len(specgram), 0, len(specgram[0])])
# show axes in Hz and seconds
time_step = hop_s / float(samplerate)
total_time = len(specgram) * time_step
outstr = "total time: %0.2fs" % total_time
print(outstr + ", samplerate: %.2fkHz" % (samplerate / 1000.))
n_xticks = 10
n_yticks = 10
def get_rounded_ticks( top_pos, step, n_ticks ):
top_label = top_pos * step
# get the first label
ticks_first_label = top_pos * step / n_ticks
# round to the closest .1
ticks_first_label = round ( ticks_first_label * 10. ) / 10.
# compute all labels from the first rounded one
ticks_labels = [ ticks_first_label * n for n in range(n_ticks) ] + [ top_label ]
# get the corresponding positions
ticks_positions = [ ticks_labels[n] / step for n in range(n_ticks) ] + [ top_pos ]
# convert to string
ticks_labels = [ "%.1f" % x for x in ticks_labels ]
# return position, label tuple to use with x/yticks
return ticks_positions, ticks_labels
# apply to the axis
x_ticks, x_labels = get_rounded_ticks ( len(specgram), time_step, n_xticks )
y_ticks, y_labels = get_rounded_ticks ( len(specgram[0]), (samplerate / 1000. / 2.) / len(specgram[0]), n_yticks )
ax.set_xticks( x_ticks )
ax.set_yticks ( y_ticks )
ax.set_xticklabels( x_labels )
ax.set_yticklabels ( y_labels )
ax.set_ylabel('Frequency (kHz)')
ax.set_xlabel('Time (s)')
ax.set_title(os.path.basename(filename))
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize('x-small')
return fig
if __name__ == '__main__':
if len(sys.argv) < 2:
print("Usage: %s <filename>" % sys.argv[0])
else:
for soundfile in sys.argv[1:]:
fig = get_spectrogram(soundfile)
# display graph
plt.show()
#outimage = os.path.basename(soundfile) + '.png'
#print ("writing: " + outimage)
#plt.savefig(outimage)
plt.close()
|
