def KaiserFil(dataserie):
import math
pi = math.pi
b = []
fs = 250
cutoff = [0.5, 4.0, 7.0, 12.0, 30.0]
y = dataserie
filtered = []
for band in range(len(cutoff) - 1):
wl = 2 * cutoff[band] / fs * pi
wh = 2 * cutoff[band + 1] / fs * pi
M = 250 # Set number of weights as 128
bn = zeros(M)
for i in range(M):
n = i - M / 2 # Make symmetrical
if n == 0:
bn[i] = wh / pi - wl / pi
else:
bn[i] = (sin(wh * n)) / (pi * n) - (sin(wl * n)) / (
pi * n) # Filter impulse response
bn = bn * kaiser(M, 5.2)
b.append(bn)
[w, h] = freqz(bn, 1)
filtered.append(np.convolve(bn, y))
#print(np.array(filtered).shape,"dd")
return np.array(filtered)[:, M // 2:M // 2 + 1000]
def load_data(
fn='/home/ritz/mix/audio samples instruments/sampled vocal/love.wav',
select=(0, 20000)):
'''Load selection of test wave file. Apply widewindow fade in/out.'''
x = wav.read(fn, to_mono=True)
x = (x[select[0]:select[1]]).astype('float128')
x = chunker.widewindow(x, pl.kaiser(1500, 15))
def smooth(x, beta):
""" kaiser window smoothing """
window_len = 11
# extending the data at beginning and at the end
# to apply the window at the borders
s = pylab.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]
w = pylab.kaiser(window_len, beta)
y = pylab.convolve(w / w.sum(), s, mode='valid')
return y[5:len(y) - 5]
def smooth(x,beta):
""" kaiser window smoothing """
window_len=11
# extending the data at beginning and at the end
# to apply the window at the borders
s = pylab.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]
w = pylab.kaiser(window_len,beta)
y = pylab.convolve(w/w.sum(),s,mode='valid')
return y[5:len(y)-5]
