lesson7-hpr.py

import numpy as np
from scipy.signal import get_window, resample
import math
import sys, os, time
from scipy.fftpack import fft, ifft
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'sms-tools-master/software/models/'))
import utilFunctions as UF
import stochasticModel as SM
import harmonicModel as HM
import dftModel as DFT

fs, x = UF.wavread('sms-tools-master/sounds/flute-A4.wav')
M = 801
N = 2048
t = -80
pin = 40000
minf0 = 300
maxf0 = 500
f0et = 5
nH = 60
harmDevSlope = 0.001

w = get_window('blackman', M)
hM1 = int(math.floor((M+1)/2))
hM2 = int(math.floor(M/2))

x1 = x[pin-hM1:pin+hM2]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
ipfreq = fs*iploc/N
f0 = UF.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, 0)
hfreq, hmag, hphase = HM.harmonicDetection(ipfreq, ipmag, ipphase, f0, nH, [], fs, harmDevSlope)

Ns = 512
hNs = 256
Yh = UF.genSpecSines(hfreq, hmag, hphase, Ns, fs)

wr = get_window('blackmanharris', Ns)
xw2 = x[pin-hNs-1:pin+hNs-1] * wr / sum(wr)
fftbuffer = np.zeros(Ns)
fftbuffer[:hNs] = xw2[hNs:]
fftbuffer[hNs:] = xw2[:hNs]
X2 = fft(fftbuffer)
Xr = X2-Yh

#hps
stocf = 0.1
mXr = 20 * np.log10(abs(Xr[:hNs]))
mXrenv = resample(np.maximum(-200, mXr), int(len(mXr) * stocf)) #downsampled
stocEnv = resample(mXrenv, hNs) #smooth residual - upsampled