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# Source Code for Module lib.zero

1  """This is code that, when given a non-negative time-series
2  corresponding to speech loudness, computes the background noise,
3  which is assumed to be constant.
4  It uses a simple model based on a Gaussian and exponential."""
5
6  import math
7  import numpy
8  import fpconst
9  from gmisclib import die
11  from gmisclib import Numeric_gpk
12
13  # import pylab
14
15 -class ZeroProblem(RuntimeError):
16 - def __init__(self, *s):
17 RuntimeError.__init__(self, *s)
18 19 20 PASSLIM = 80 21
22 -def exp(x):
23 return numpy.exp(numpy.maximum(x, -50.0))
24
25 -def find_zero(x, debug=None):
26 """Models the loudness as being drawn either from a Gaussian 27 distribution (corresponding to the background noise level) 28 or a positive exponential distribtion (corresponding to speech). 29 It returns parameters of these distributions. 30 31 @param x: a time-series of loudness. 32 @return: four floats: (1) the estimate of the background noise level, 33 (2) the width of the noise Gaussian, (3) the mean speech level, 34 (4) the fraction of time with no speech (just background noise). 35 return (zz, sz, e, Pz) 36 @rtype: tuple(float, float, float, float) 37 """ 38 zz = Numeric_gpk.N_minimum(x) 39 sigma2 = numpy.var(x) 40 if not (sigma2 > 0.0): 41 raise ZeroProblem, "Loudness zero has sigma=zero -- seemingly no signal here." 42 sz2 = sigma2/64.0 43 sv2 = sigma2 * 2.0 44 #: fz is the estimated fraction of data that are at zero loudness, i.e. part of the 45 #: Gaussian centered at zero. 46 fz = 0.5 47 ipass = 0 48 q = 1000.0 49 while ipass<PASSLIM and q>0.0001: 50 assert 0.0 <= fz <= 1.0 51 ozz = zz 52 osz2 = sz2 53 osv2 = sv2 54 ofz = fz 55 56 gzz = numpy.greater(x, zz) 57 assert sv2 > 0.0 58 assert sz2 > 0.0 59 wv = gzz * exp(-numpy.square(x-zz)/(2*sv2))/math.sqrt(2*math.pi*sv2) 60 wz = exp(-numpy.square(x-zz)/(2*sz2))/math.sqrt(2*math.pi*sz2) 61 #: flatfudge is a fudge factor that expresses the idea that the "zero" regions 62 #: are fairly flat. 63 flatfudge = exp(-numpy.square(x[1:]-x[:-1])/(32*sz2)) 64 numpy.multiply(wz[1:], flatfudge, wz[1:]) 65 numpy.multiply(wz[:-1], flatfudge, wz[:-1]) 66 assert 0.0 < fz < 1.0 67 pz = fz*wz / (fz*wz + (1-fz)*wv) 68 69 speed = math.exp(-2.5*ipass/float(PASSLIM)) 70 fz = (1-speed)*fz + speed*min(0.99, max(0.01, numpy.average(pz))) 71 pv = 1.0 -pz 72 sumPv = numpy.sum(pv) 73 sumPz = numpy.sum(pz) 74 if fpconst.isNaN(sumPz) or fpconst.isNaN(sumPv): 75 raise ZeroProblem, "NaN while computing zero: sumPz sumPv: ipass=%d" % ipass 76 zz = (1-speed)*zz + speed * numpy.sum(pz*x) / sumPz 77 sz2 = max(1e-4*sigma2, (1-speed)*sz2 + speed*numpy.sum(pz*numpy.square(x-zz))/sumPz) 78 sv2 = max(sz2, (1-speed)*sv2 + speed*numpy.sum(pv*numpy.square(x-zz))/sumPv) 79 if debug is not None: 80 debug(x, pz, zz, fz, sz2, sv2, ipass) 81 82 q = (ozz-zz)**2/(sz2+osz2) + math.log(sz2/osz2)**2 + math.log(sv2/osv2)**2 + math.log(fz/ofz)**2 83 ipass += 1 84 if debug is not None: 85 debug(x, pz, zz, fz, sz2, sv2, -1) 86 sz = math.sqrt(sz2) 87 sv = math.sqrt(sv2) 88 if ipass >= PASSLIM: 89 raise ZeroProblem, "Loudness zero does not converge: %g %g %g %g" % (zz, sz, sv, fz) 90 if sv<3*sz or zz<0.10*sz or zz>sv/2.0 or fz>0.9: 91 raise ZeroProblem, "Strange loudness zero: %g %g %g %g" % (zz, sz, sv, fz) 92 return (zz, sz, sv, fz)
93 94
95 -def loud_zero(x, debug=None):
96 """@param x: a time-series of loudness. 97 @return: An estimate of the background noise level 98 @rtype: float 99 """ 100 zz, sz, e, Pz = find_zero(x, debug=debug) 101 return zz
102 103
104 -def zero_sub(x):
105 return numpy.maximum(x**3 - loud_zero(x)**3, 0.0)**(1./3.)
106 107
108 -def percep_spec_zero(x):
109 assert len(x.shape)==2 and x.shape[0]<60 110 loud = numpy.sum(x, axis=0) 111 zz, sz, e, Pz = find_zero(loud) 112 fz = Pz.sum() 113 return numpy.sum(x*Pz[numpy.newaxis,:], axis=1)/fz
114 115
116 -def diagnostic_plot(x, ex):
117 pylab = load_mod.load_named('pylab') 118 for i in range(x.shape[0]): 119 pylab.plot(x[i,:]) 120 pylab.title(str(ex)) 121 pylab.show()
122 123 124 if __name__ == '__main__': 125 import sys 126 import pylab 127 import gpkimgclass 128 d = gpkimgclass.read(sys.argv[1]) 129 x = d.column(0)/numpy.average(d.column(0))
130 - def debug(x, pz, zz, fz, sz2, sv2, i):
131 if debug < 0: 132 return 133 pylab.figure(i) 134 pylab.plot(x, 'b-') 135 pylab.plot(pz, 'r-') 136 pylab.title('zz=%g fz=%.3f sz=%.3f sv=%.3f, i=%d' % (zz, fz, math.sqrt(sz2), math.sqrt(sv2), i))
137 try: 138 zz, sz, sv, pz = find_zero(x, debug) 139 except ZeroProblem, x: 140 print "Zero problem: %s" % x 141 pylab.show() 142

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