Source Code for Module lib.fvss

 1  #!/usr/bin/env python 
 2   
 3   
 4  import numpy 
 5  from gmisclib import erb_scale 
 6  from gpk_voicing import percep_spec 
 7  from gpk_voicing import fv_misc as M 
 8  SillyWidthException = M.SillyWidthException 
 9   
10   
11 -def feature_vec(data, dt, DT, 
12                  LF=1.0, Nsv=M.NSV, ELF=None, 
13                  do_voicing=1, do_dissonance=False, PlompBouman=True): 
14          FORMANT_LOW = erb_scale.f_to_erb(200.0) 
15          FORMANT_HIGH = erb_scale.f_to_erb(4000.0) 
16          assert DT > 0.0 and float(DT)>0.0 
17          assert LF > 0.0 and float(LF)>0.0 
18          bmin = erb_scale.f_to_erb(100.0) 
19          bmax = erb_scale.f_to_erb(6000.0) 
20          all_ectrs, all_ps, t0 = percep_spec.perceptual_spec(data, dt, DT, 
21                                                          bmin, bmax, M.DB, 
22                                                          do_mod=do_voicing, 
23                                                          do_dissonance=False, 
24                                                          PlompBouman=PlompBouman 
25                                                          ) 
26   
27          band_indices = [i for (i,ec) in enumerate(all_ectrs) if ec['type']=='band'] 
28          # print 'band_indices=', band_indices 
29          neural = all_ps.take(band_indices, axis=0) 
30          ectrs = [ec for ec in all_ectrs if ec['type']=='band'] 
31          # print 'ectrs=', ectrs 
32          nband_indices = [i for (i,ec) in enumerate(all_ectrs) if ec['type']!='band'] 
33          # print 'nband_indices=', nband_indices 
34          nneural = all_ps.take(nband_indices, axis=0) 
35          nectrs = [ec for ec in all_ectrs if ec['type']!='band'] 
36          # print 'nectrs=', nectrs 
37   
38          assert nneural.shape[1]==neural.shape[1] 
39          assert neural.shape[1]==all_ps.shape[1] 
40          assert neural.shape[0]+nneural.shape[0] == all_ps.shape[0] 
41   
42          neural_now = numpy.average(neural, axis=0) 
43          # pylab.plot(neural_now) 
44          # pylab.title('neural_now') 
45          # pylab.show() 
46          assert neural_now.shape[0] == neural.shape[1] 
47          neural_avg = numpy.square(neural_now).sum()/numpy.sum(neural_now) 
48          # print 'neural_avg=', neural_avg 
49          numpy.divide(neural, neural_avg, neural) 
50          numpy.divide(neural_now, neural_avg, neural_now) 
51          # pylab.plot(neural_now) 
52          # pylab.title('normed neural_now') 
53          # pylab.show() 
54   
55          assert nneural.shape[0] < nneural.shape[1] 
56          assert len(nectrs) == nneural.shape[0] 
57          for (i,e) in enumerate(nectrs): 
58                  if e['type'] == 'haspitch': 
59                          numpy.divide(nneural[i,:], neural_avg, nneural[i,:]) 
60                  if e['type'] == 'dissonance': 
61                          numpy.divide(nneural[i,:], neural_avg, nneural[i,:]) 
62   
63          # print '# neural_avg=', neural_avg 
64          o = [] 
65          descr = [] 
66          Vl = 0.06 
67          w = int(round(Vl*LF/DT)) 
68          tmpo, tmpd = M.vowel(w, ectrs, neural, neural_now, Nsv, 
69                                                  formant_low=FORMANT_LOW, 
70                                                  formant_high=FORMANT_HIGH 
71                                                  ) 
72          o.extend(tmpo) 
73          descr.extend(tmpd) 
74          assert len(descr)==len(o), "Descriptor mismatch" 
75   
76          if do_dissonance: 
77                  raise RuntimeError, "Assumes dissonance=False" 
78   
79          if do_voicing: 
80                  Hpl = 0.07 
81                  w = int(round(Hpl*LF/DT)) 
82                  tmpo, tmpd = M.haspitch(w, nectrs, nneural, neural_now, Nsv) 
83                  o.extend(tmpo) 
84                  descr.extend(tmpd) 
85   
86          assert len(descr)==len(o), "Descriptor mismatch" 
87          N = neural[0].shape[0] 
88          for (i, (tmp, dsc)) in enumerate(zip(o, descr)): 
89                  assert tmp.shape == (N,), "Wrong size: %d, %s = %d vs. %d" % (i, str(dsc), tmp.shape[0], N) 
90   
91          return (o, descr, DT, t0) 
92