1#! /usr/bin/env python 2 3import sys, os.path 4from aubio import pvoc, source, float_type 5from numpy import zeros, log10, vstack 6import matplotlib.pyplot as plt 7 8def get_spectrogram(filename, samplerate = 0): 9 win_s = 512 # fft window size 10 hop_s = win_s // 2 # hop size 11 fft_s = win_s // 2 + 1 # spectrum bins 12 13 a = source(filename, samplerate, hop_s) # source file 14 if samplerate == 0: samplerate = a.samplerate 15 pv = pvoc(win_s, hop_s) # phase vocoder 16 specgram = zeros([0, fft_s], dtype=float_type) # numpy array to store spectrogram 17 18 # analysis 19 while True: 20 samples, read = a() # read file 21 specgram = vstack((specgram,pv(samples).norm)) # store new norm vector 22 if read < a.hop_size: break 23 24 # plotting 25 fig = plt.imshow(log10(specgram.T + .001), origin = 'bottom', aspect = 'auto', cmap=plt.cm.gray_r) 26 ax = fig.axes 27 ax.axis([0, len(specgram), 0, len(specgram[0])]) 28 # show axes in Hz and seconds 29 time_step = hop_s / float(samplerate) 30 total_time = len(specgram) * time_step 31 outstr = "total time: %0.2fs" % total_time 32 print(outstr + ", samplerate: %.2fkHz" % (samplerate / 1000.)) 33 n_xticks = 10 34 n_yticks = 10 35 36 def get_rounded_ticks( top_pos, step, n_ticks ): 37 top_label = top_pos * step 38 # get the first label 39 ticks_first_label = top_pos * step / n_ticks 40 # round to the closest .1 41 ticks_first_label = round ( ticks_first_label * 10. ) / 10. 42 # compute all labels from the first rounded one 43 ticks_labels = [ ticks_first_label * n for n in range(n_ticks) ] + [ top_label ] 44 # get the corresponding positions 45 ticks_positions = [ ticks_labels[n] / step for n in range(n_ticks) ] + [ top_pos ] 46 # convert to string 47 ticks_labels = [ "%.1f" % x for x in ticks_labels ] 48 # return position, label tuple to use with x/yticks 49 return ticks_positions, ticks_labels 50 51 # apply to the axis 52 x_ticks, x_labels = get_rounded_ticks ( len(specgram), time_step, n_xticks ) 53 y_ticks, y_labels = get_rounded_ticks ( len(specgram[0]), (samplerate / 1000. / 2.) / len(specgram[0]), n_yticks ) 54 ax.set_xticks( x_ticks ) 55 ax.set_yticks ( y_ticks ) 56 ax.set_xticklabels( x_labels ) 57 ax.set_yticklabels ( y_labels ) 58 ax.set_ylabel('Frequency (kHz)') 59 ax.set_xlabel('Time (s)') 60 ax.set_title(os.path.basename(filename)) 61 for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + 62 ax.get_xticklabels() + ax.get_yticklabels()): 63 item.set_fontsize('x-small') 64 return fig 65 66if __name__ == '__main__': 67 if len(sys.argv) < 2: 68 print("Usage: %s <filename>" % sys.argv[0]) 69 else: 70 for soundfile in sys.argv[1:]: 71 fig = get_spectrogram(soundfile) 72 # display graph 73 plt.show() 74 #outimage = os.path.basename(soundfile) + '.png' 75 #print ("writing: " + outimage) 76 #plt.savefig(outimage) 77 plt.close() 78