#! /usr/bin/env python
-import sys
+import sys, os.path
from aubio import pvoc, source
-from numpy import array, arange, zeros, shape, log10, vstack
-from pylab import imshow, show, cm, axis, ylabel, xlabel, xticks, yticks
+from numpy import array, arange, zeros, log10, vstack
+import matplotlib.pyplot as plt
-def get_spectrogram(filename):
- samplerate = 44100
- win_s = 512 # fft window size
- hop_s = win_s / 2 # hop size
- fft_s = win_s / 2 + 1 # spectrum bins
+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
- pv = pvoc(win_s, hop_s) # phase vocoder
- specgram = zeros([0, fft_s], dtype='float32') # numpy array to store spectrogram
+ 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='float32') # 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
+ # 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
- imshow(log10(specgram.T + .001), origin = 'bottom', aspect = 'auto', cmap=cm.gray_r)
- 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
- print "total time: %0.2fs" % total_time,
- print ", samplerate: %.2fkHz" % (samplerate / 1000.)
- n_xticks = 10
- n_yticks = 10
+ # 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
+ print "total time: %0.2fs" % total_time,
+ print ", 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
- xticks( *get_rounded_ticks ( len(specgram), time_step, n_xticks ) )
- yticks( *get_rounded_ticks ( len(specgram[0]), (samplerate / 2. / 1000.) / len(specgram[0]), n_yticks ) )
- ylabel('Frequency (kHz)')
- xlabel('Time (s)')
+ 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:]:
- get_spectrogram(soundfile)
- # display graph
- show()
+ 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
+ fig.show()
+ #outimage = os.path.basename(soundfile) + '.png'
+ #print ("writing: " + outimage)
+ #plt.savefig(outimage)
+ plt.close()
projects / aubio.git / blobdiff