Source Code for Module lib.fv200912opt

  1  #!/usr/bin/env python 
  2   
  3  """Feature vectors for optimized DTW alignment. 
  4  Exemplar project with Ladan Baghai-Ravary. 
  5  """ 
  6   
  7  import math 
  8  import numpy 
  9  from gmisclib import die 
 10  from gmisclib import erb_scale 
 11  from gpk_voicing import percep_spec 
 12  from gpk_voicing import fv_misc as M 
 13  from gpk_voicing import zero as Z 
 14  SillyWidthException = M.SillyWidthException 
 15   
 16  DB = 0.85 
 17   
 18  # Scale = M.scale_xform(Opt_text, 
 19                          # name="299010 optimization in /proj/exemplar/ItakuraSaitoDistance/IS-DTW/log_adv,,-fv200909opt_0.av" 
 20                          # ) 
 21   
 22   
 23   
 24 -def feature_vec(data, dt, DT, 
 25                  LF=1.0, Nsv=M.NSV, ELF=1.0, 
 26                  do_voicing=1, do_dissonance=False, 
 27                  PlompBouman=False, do_pdur=False, zerosub=0.0): 
 28          assert not do_pdur and not do_dissonance 
 29          FORMANT_LOW = erb_scale.f_to_erb(120.0) 
 30          FORMANT_HIGH = erb_scale.f_to_erb(5000.0) 
 31          assert float(DT)>0.0 
 32          assert float(LF)>0.0 
 33          bmin = erb_scale.f_to_erb(80.0) 
 34          bmax = erb_scale.f_to_erb(6000.0) 
 35          ectrs, neural, t0 = percep_spec.perceptual_spec(data, dt, DT, 
 36                                                          bmin, bmax, DB, 
 37                                                          do_mod=do_voicing, 
 38                                                          do_dissonance=do_dissonance, 
 39                                                          do_peakalign=True, 
 40                                                          PlompBouman=PlompBouman 
 41                                                          ) 
 42   
 43          assert 1 < neural.shape[0] < 200 
 44          band_indices = [i for (i,ec) in enumerate(ectrs) if ec['type']=='band'] 
 45          neural_b = neural.take(band_indices, axis=0) 
 46          assert neural_b.shape[1]==neural.shape[1] 
 47          if zerosub != 0.0: 
 48                  neural_b = neural_b.copy() 
 49                  try: 
 50                          z = Z.percep_spec_zero(neural_b) 
 51                  except Z.ZeroProblem: 
 52                          die.warn("Bad utterance: zero does not converge.") 
 53                          z = numpy.zeros((neural_b.shape[0],)) 
 54                  assert neural_b.shape[0]==z.shape[0] 
 55                  numpy.subtract(neural_b, z[:,numpy.newaxis], neural_b) 
 56          assert neural_b.shape[1]==neural.shape[1] 
 57          assert neural_b.shape[0]<=neural.shape[0] 
 58   
 59          neural_now = numpy.average(neural_b, axis=0)    # Average over frequency. 
 60          assert neural_now.shape[0] == neural.shape[1] 
 61          neural_avg = numpy.sum(neural_now**2)/numpy.sum(neural_now)     # Average over time. 
 62                  # neural_avg is a scalar, grand average. 
 63          numpy.divide(neural, neural_avg, neural) 
 64                  # Now, we've normalized by an over-all average loudness. 
 65          numpy.divide(neural_now, neural_avg, neural_now) 
 66                  # Now, we've normalized by an over-all average loudness. 
 67   
 68          for (i,e) in enumerate(ectrs): 
 69                  if e['type'] == 'haspitch': 
 70                          numpy.divide(neural[i,:], neural_avg, neural[i,:]) 
 71                  if e['type'] == 'dissonance': 
 72                          numpy.divide(neural[i,:], neural_avg, neural[i,:]) 
 73                  if e['type'] == 'peakalign': 
 74                          numpy.divide(neural[i,:], neural_avg**2, neural[i,:]) 
 75   
 76          # print '# neural_avg=', neural_avg 
 77          o = [] 
 78          descr = [] 
 79          w = 0.04*LF/DT 
 80          tmpo, tmpd = M.vowel_edge(w, ectrs, neural, neural_now, Nsv, 
 81                                                  do_abs=False, 
 82                                                  formant_low=FORMANT_LOW, 
 83                                                  formant_high=FORMANT_HIGH 
 84                                                  ) 
 85          o.extend(tmpo) 
 86          descr.extend(tmpd) 
 87          assert len(descr)==len(o), "Descriptor mismatch" 
 88   
 89          w = 0.03*ELF/DT 
 90          tmpo, tmpd = M.fricative(w, ectrs, neural, neural_now, Nsv) 
 91          o.extend(tmpo) 
 92          descr.extend(tmpd) 
 93          assert len(descr)==len(o), "Descriptor mismatch" 
 94   
 95          if do_voicing: 
 96                  w = 0.02*math.sqrt(LF)/DT 
 97                  tmpo, tmpd = M.haspitch(w, ectrs, neural, neural_now, Nsv) 
 98                  o.extend(tmpo) 
 99                  descr.extend(tmpd) 
100          assert len(descr)==len(o), "Descriptor mismatch" 
101   
102          w = 0.03*ELF/DT 
103          tmpo, tmpd = M.peakiness(w, ectrs, neural, neural_now, Nsv) 
104          o.extend(tmpo) 
105          descr.extend(tmpd) 
106          assert len(descr)==len(o), "Descriptor mismatch" 
107   
108          N = neural[0].shape[0] 
109          for (i, (tmp, dsc)) in enumerate(zip(o, descr)): 
110                  assert tmp.shape == (N,), "Wrong size: %d, %s = %d vs. %d" % (i, str(dsc), tmp.shape[0], N) 
111   
112          return (o, descr, DT, t0) 
113