--- /dev/null
+#! /usr/bin/env python
+
+import sys
+from aubio import fvec, source, pvoc, filterbank
+from numpy import vstack, zeros
+
+win_s = 512 # fft size
+hop_s = win_s / 4 # hop size
+
+if len(sys.argv) < 2:
+ print "Usage: %s <filename> [samplerate]" % sys.argv[0]
+ sys.exit(1)
+
+filename = sys.argv[1]
+
+samplerate = 0
+if len( sys.argv ) > 2: samplerate = int(sys.argv[2])
+
+s = source(filename, samplerate, hop_s)
+samplerate = s.samplerate
+
+pv = pvoc(win_s, hop_s)
+
+f = filterbank(40, win_s)
+f.set_mel_coeffs_slaney(samplerate)
+
+energies = zeros((40,))
+o = {}
+
+total_frames = 0
+downsample = 2
+
+while True:
+ samples, read = s()
+ fftgrain = pv(samples)
+ new_energies = f(fftgrain)
+ print '%f' % (total_frames / float(samplerate) ),
+ print ' '.join(['%f' % b for b in new_energies])
+ energies = vstack( [energies, new_energies] )
+ total_frames += read
+ if read < hop_s: break
+
+if 1:
+ print "done computing, now plotting"
+ import matplotlib.pyplot as plt
+ from demo_waveform_plot import get_waveform_plot
+ from demo_waveform_plot import set_xlabels_sample2time
+ fig = plt.figure()
+ plt.rc('lines',linewidth='.8')
+ wave = plt.axes([0.1, 0.75, 0.8, 0.19])
+ get_waveform_plot(filename, samplerate, block_size = hop_s, ax = wave )
+ wave.yaxis.set_visible(False)
+ wave.xaxis.set_visible(False)
+
+ n_plots = len(energies.T)
+ all_desc_times = [ x * hop_s for x in range(len(energies)) ]
+ for i, band in enumerate(energies.T):
+ ax = plt.axes ( [0.1, 0.75 - ((i+1) * 0.65 / n_plots), 0.8, 0.65 / n_plots], sharex = wave )
+ ax.plot(all_desc_times, band, '-', label = 'band %d' % i)
+ #ax.set_ylabel(method, rotation = 0)
+ ax.xaxis.set_visible(False)
+ ax.yaxis.set_visible(False)
+ ax.axis(xmax = all_desc_times[-1], xmin = all_desc_times[0])
+ ax.annotate('band %d' % i, xy=(-10, 10), xycoords='axes points',
+ horizontalalignment='right', verticalalignment='bottom',
+ )
+ set_xlabels_sample2time( ax, all_desc_times[-1], samplerate)
+ #plt.ylabel('spectral descriptor value')
+ ax.xaxis.set_visible(True)
+ plt.show()
--- /dev/null
+#! /usr/bin/env python
+
+import sys
+from aubio import source, pvoc, mfcc
+from numpy import array, vstack, zeros
+
+win_s = 512 # fft size
+hop_s = win_s / 4 # hop size
+n_filters = 40
+n_coeffs = 13
+samplerate = 44100
+
+if len(sys.argv) < 2:
+ print "Usage: %s <source_filename>" % sys.argv[0]
+ sys.exit(1)
+
+source_filename = sys.argv[1]
+
+samplerate = 0
+if len( sys.argv ) > 2: samplerate = int(sys.argv[2])
+
+s = source(source_filename, samplerate, hop_s)
+samplerate = s.samplerate
+p = pvoc(win_s, hop_s)
+m = mfcc(win_s, n_filters, n_coeffs, samplerate)
+
+desc = []
+tdesc = []
+
+mfccs = zeros([13,])
+frames_read = 0
+while True:
+ samples, read = s()
+ spec = p(samples)
+ mfcc_out = m(spec)
+ mfccs = vstack((mfccs, mfcc_out))
+ frames_read += read
+ if read < hop_s: break
+
+# do plotting
+from numpy import arange
+from demo_waveform_plot import get_waveform_plot
+from demo_waveform_plot import set_xlabels_sample2time
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+plt.rc('lines',linewidth='.8')
+wave = plt.axes([0.1, 0.75, 0.8, 0.19])
+
+get_waveform_plot( source_filename, samplerate, block_size = hop_s, ax = wave)
+wave.xaxis.set_visible(False)
+wave.yaxis.set_visible(False)
+
+all_times = arange(mfccs.shape[0]) * hop_s
+n_coeffs = mfccs.shape[1]
+for i in range(n_coeffs):
+ ax = plt.axes ( [0.1, 0.75 - ((i+1) * 0.65 / n_coeffs), 0.8, 0.65 / n_coeffs], sharex = wave )
+ ax.xaxis.set_visible(False)
+ ax.yaxis.set_visible(False)
+ ax.plot(all_times, mfccs.T[i])
+
+# add time to the last axis
+set_xlabels_sample2time( ax, frames_read, samplerate)
+
+#plt.ylabel('spectral descriptor value')
+ax.xaxis.set_visible(True)
+wave.set_title('MFCC for %s' % source_filename)
+plt.show()
pv = pvoc(win_s, hop_s)
-methods = ['default', 'energy', 'hfc', 'complex', 'phase', 'specdiff', 'kl', 'mkl',
- 'specflux', 'centroid', 'spread', 'skewness', 'kurtosis', 'slope', 'decrease',
- 'rolloff', ]
+methods = ['default', 'energy', 'hfc', 'complex', 'phase', 'specdiff', 'kl',
+ 'mkl', 'specflux', 'centroid', 'slope', 'rolloff', 'spread', 'skewness',
+ 'kurtosis', 'decrease',]
all_descs = {}
o = {}
while True:
samples, read = s()
fftgrain = pv(samples)
- print "%f" % ( total_frames / float(samplerate) ),
+ #print "%f" % ( total_frames / float(samplerate) ),
for method in methods:
specdesc_val = o[method](fftgrain)[0]
all_descs[method] = hstack ( [all_descs[method], specdesc_val] )
- print "%f" % specdesc_val,
- print
+ #print "%f" % specdesc_val,
+ #print
total_frames += read
if read < hop_s: break
print "done computing, now plotting"
import matplotlib.pyplot as plt
from demo_waveform_plot import get_waveform_plot
+ from demo_waveform_plot import set_xlabels_sample2time
fig = plt.figure()
plt.rc('lines',linewidth='.8')
wave = plt.axes([0.1, 0.75, 0.8, 0.19])
- get_waveform_plot(filename, samplerate, ax = wave )
+ get_waveform_plot(filename, samplerate, block_size = hop_s, ax = wave )
wave.yaxis.set_visible(False)
wave.xaxis.set_visible(False)
ax.annotate(method, xy=(-10, 10), xycoords='axes points',
horizontalalignment='right', verticalalignment='bottom',
)
- if all_desc_times[-1] / float(samplerate) > 60:
- plt.xlabel('time (mm:ss)')
- ax.set_xticklabels([ "%02d:%02d" % (t/float(samplerate)/60, (t/float(samplerate))%60) for t in ax.get_xticks()[:-1]], rotation = 50)
- else:
- plt.xlabel('time (ss.mm)')
- ax.set_xticklabels([ "%02d.%02d" % (t/float(samplerate), 100*((t/float(samplerate))%1) ) for t in ax.get_xticks()[:-1]], rotation = 50)
+ set_xlabels_sample2time(ax, all_desc_times[-1], samplerate)
#plt.ylabel('spectral descriptor value')
ax.xaxis.set_visible(True)
plt.show()
from aubio import pvoc, source
from numpy import zeros, hstack
-def get_waveform_plot(filename, samplerate = 0, ax = None):
+def get_waveform_plot(filename, samplerate = 0, block_size = 4096, ax = None):
import matplotlib.pyplot as plt
if not ax:
fig = plt.figure()
ax = fig.add_subplot(111)
- hop_s = 4096 # block size
+ hop_s = block_size
allsamples_max = zeros(0,)
- downsample = 2**3 # to plot n samples / hop_s
+ downsample = 2**4 # to plot n samples / hop_s
a = source(filename, samplerate, hop_s) # source file
if samplerate == 0: samplerate = a.samplerate
allsamples_max = hstack([allsamples_max, new_maxes])
total_frames += read
if read < hop_s: break
- print samples.reshape(hop_s/downsample, downsample).shape
-
allsamples_max = (allsamples_max > 0) * allsamples_max
allsamples_max_times = [ ( float (t) / downsample ) * hop_s for t in range(len(allsamples_max)) ]
ax.plot(allsamples_max_times, -allsamples_max, '-b')
ax.axis(xmin = allsamples_max_times[0], xmax = allsamples_max_times[-1])
- if allsamples_max_times[-1] / float(samplerate) > 60:
+ set_xlabels_sample2time(ax, allsamples_max_times[-1], samplerate)
+ return ax
+
+def set_xlabels_sample2time(ax, latest_sample, samplerate):
+ ax.axis(xmin = 0, xmax = latest_sample)
+ if latest_sample / float(samplerate) > 60:
ax.set_xlabel('time (mm:ss)')
ax.set_xticklabels([ "%02d:%02d" % (t/float(samplerate)/60, (t/float(samplerate))%60) for t in ax.get_xticks()[:-1]], rotation = 50)
else:
ax.set_xlabel('time (ss.mm)')
ax.set_xticklabels([ "%02d.%02d" % (t/float(samplerate), 100*((t/float(samplerate))%1) ) for t in ax.get_xticks()[:-1]], rotation = 50)
+
+
if __name__ == '__main__':
import matplotlib.pyplot as plt
if len(sys.argv) < 2: