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import math
import logging
import numpy as np
from matplotlib import mlab
from scipy import interpolate
from decimal import Decimal, ROUND_HALF_UP
logging.getLogger("matplotlib").setLevel(logging.ERROR)
def swipe(x, fs, f0_floor=50, f0_ceil=1100, frame_period=10, sTHR=0.3):
plim = np.array([f0_floor, f0_ceil])
t = np.arange(0, int(1000 * len(x) / fs / (frame_period) + 1)) * (frame_period / 1000)
log2pc = np.arange(np.log2(plim[0]) * 96, np.log2(plim[-1]) * 96)
log2pc *= (1 / 96)
pc = 2 ** log2pc
S = np.zeros((len(pc), len(t)))
logWs = [round_matlab(elm) for elm in np.log2(4 * 2 * fs / plim)]
ws = 2 ** np.arange(logWs[0], logWs[1] - 1, -1)
p0 = 4 * 2 * fs / ws
d = 1 + log2pc - np.log2(4 * 2 * fs / ws[0])
fERBs = erbs2hz(np.arange(hz2erbs(pc[0] / 4), hz2erbs(fs / 2), 0.1))
for i in range(len(ws)):
dn = round_matlab(4 * fs / p0[i])
X, f, ti = mlab.specgram(x=np.r_[np.zeros(int(ws[i] / 2)), np.r_[x, np.zeros(int(dn + ws[i] / 2))]], NFFT=ws[i], Fs=fs, window=np.hanning(ws[i] + 2)[1:-1], noverlap=max(0, np.round(ws[i] - dn)), mode='complex')
ti = np.r_[0, ti[:-1]]
M = np.maximum(0, interpolate.interp1d(f, np.abs(X.T), kind='cubic')(fERBs)).T
if i == len(ws) - 1:
j = np.where(d - (i + 1) > -1)[0]
k = np.where(d[j] - (i + 1) < 0)[0]
elif i == 0:
j = np.where(d - (i + 1) < 1)[0]
k = np.where(d[j] - (i + 1) > 0)[0]
else:
j = np.where(np.abs(d - (i + 1)) < 1)[0]
k = np.arange(len(j))
Si = pitchStrengthAllCandidates(fERBs, np.sqrt(M), pc[j])
Si = interpolate.interp1d(ti, Si, bounds_error=False, fill_value='nan')(t) if Si.shape[1] > 1 else np.full((len(Si), len(t)), np.nan)
mu = np.ones(j.shape)
mu[k] = 1 - np.abs(d[j[k]] - i - 1)
S[j, :] = S[j, :] + np.tile(mu.reshape(-1, 1), (1, Si.shape[1])) * Si
p = np.full((S.shape[1], 1), np.nan)
s = np.full((S.shape[1], 1), np.nan)
for j in range(S.shape[1]):
s[j] = np.max(S[:, j])
i = np.argmax(S[:, j])
if s[j] < sTHR: continue
if i == 0: p[j] = pc[0]
elif i == len(pc) - 1: p[j] = pc[0]
else:
I = np.arange(i-1, i+2)
tc = 1 / pc[I]
ntc = (tc / tc[1] - 1) * 2 * np.pi
idx = np.isfinite(S[I, j])
c = np.zeros(len(ntc))
c += np.nan
I_ = I[idx]
if len(I_) < 2: c[idx] = (S[I, j])[0] / ntc[0]
else: c[idx] = np.polyfit(ntc[idx], (S[I_, j]), 2)
pval = np.polyval(c, ((1 / (2 ** np.arange(np.log2(pc[I[0]]), np.log2(pc[I[2]]) + 1 / 12 / 64, 1 / 12 / 64))) / tc[1] - 1) * 2 * np.pi)
s[j] = np.max(pval)
p[j] = 2 ** (np.log2(pc[I[0]]) + (np.argmax(pval)) / 12 / 64)
p = p.flatten()
p[np.isnan(p)] = 0
return np.array(p, dtype=np.float32), np.array(t, dtype=np.float32)
def round_matlab(n):
return int(Decimal(n).quantize(0, ROUND_HALF_UP))
def pitchStrengthAllCandidates(f, L, pc):
den = np.sqrt(np.sum(L * L, axis=0))
den = np.where(den == 0, 2.220446049250313e-16, den)
L = L / den
S = np.zeros((len(pc), L.shape[1]))
for j in range(len(pc)):
S[j,:] = pitchStrengthOneCandidate(f, L, pc[j])
return S
def pitchStrengthOneCandidate(f, L, pc):
k = np.zeros(len(f))
q = f / pc
for i in ([1] + sieve(int(np.fix(f[-1] / pc - 0.75)))):
a = np.abs(q - i)
p = a < 0.25
k[p] = np.cos(2 * np.pi * q[p])
v = np.logical_and((0.25 < a), (a < 0.75))
k[v] = k[v] + np.cos(2 * np.pi * q[v]) / 2
k *= np.sqrt(1 / f)
k /= np.linalg.norm(k[k>0])
return k @ L
def hz2erbs(hz):
return 21.4 * np.log10(1 + hz / 229)
def erbs2hz(erbs):
return (10 ** (erbs / 21.4) - 1) * 229
def sieve(n):
primes = list(range(2, n+1))
num = 2
while num < math.sqrt(n):
i = num
while i <= n:
i += num
if i in primes: primes.remove(i)
for j in primes:
if j > num:
num = j
break
return primes |