ref: 89bc6f4938d3c1e41e351d7cd8314ed3fb3cd433
dir: /python/demos/demo_spectrogram.py/
#! /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()