def test_xcorr():
x = array([1, 2, 3, 4, 5])
corr, l = xcorr(x, x, maxlags=4, norm='coeff')
corr_coeff = array([
0.09090909, 0.25454545, 0.47272727, 0.72727273, 1., 0.72727273,
0.47272727, 0.25454545, 0.09090909
])
assert_array_almost_equal(corr, corr_coeff)
corr, l = xcorr(x, x, maxlags=4, norm='biased')
corr_biased = array([1., 2.8, 5.2, 8., 11., 8., 5.2, 2.8, 1.])
assert_array_almost_equal(corr, corr_biased)
corr, l = xcorr(x, x, maxlags=4, norm='unbiased')
corr_unbiased = array(
[5., 7., 8.66666667, 10., 11., 10., 8.66666667, 7., 5.])
assert_array_almost_equal(corr, corr_unbiased)
corr, l = xcorr(x, x, maxlags=4, norm=None)
corr_none = array([5, 14, 26, 40, 55, 40, 26, 14, 5])
assert_array_almost_equal(corr, corr_none)
# check default behaviour of maxlags
corr1, l = xcorr(x, x)
corr2, l = xcorr(x, x, maxlags=4)
assert_array_almost_equal(corr1, corr2)
# check default behaviour of maxlags
corr1, l = xcorr(x)
corr2, l = xcorr(x, x)
assert_array_almost_equal(corr1, corr2)
def test_xcorr():
x = array([1,2,3,4,5])
corr,l = xcorr(x, x, maxlags=4, norm='coeff')
corr_coeff = array([ 0.09090909, 0.25454545, 0.47272727, 0.72727273, 1. , 0.72727273, 0.47272727, 0.25454545, 0.09090909])
assert_array_almost_equal(corr, corr_coeff)
corr,l = xcorr(x, x, maxlags=4, norm='biased')
corr_biased = array([ 1. , 2.8, 5.2, 8. , 11. , 8. , 5.2, 2.8, 1. ])
assert_array_almost_equal(corr, corr_biased)
corr,l = xcorr(x, x, maxlags=4, norm='unbiased')
corr_unbiased = array([ 5. , 7. , 8.66666667, 10. , 11. , 10. , 8.66666667, 7. , 5. ])
assert_array_almost_equal(corr, corr_unbiased)
corr,l = xcorr(x, x, maxlags=4, norm=None)
corr_none = array([ 5, 14, 26, 40, 55, 40, 26, 14, 5])
assert_array_almost_equal(corr, corr_none)
# check default behaviour of maxlags
corr1,l = xcorr(x, x)
corr2,l = xcorr(x, x, maxlags=4)
assert_array_almost_equal(corr1, corr2)
# check default behaviour of maxlags
corr1,l = xcorr(x)
corr2,l = xcorr(x, x)
assert_array_almost_equal(corr1, corr2)
def _test_xcorr_versus_CORRELATION_imag_data():
from spectrum.tools import twosided as func
x = array([1, 2, 3, 4, 5 + 1.j])
for norm in ['biased', 'unbiased', 'coeff', None]:
corr1, l = xcorr(x, x, maxlags=4, norm=norm)
corr2 = CORRELATION(x, x, maxlags=4, norm=norm)
assert_array_almost_equal(corr1, func(corr2))
def _test_xcorr_versus_CORRELATION_imag_data():
from spectrum.tools import twosided as func
x = array([1,2,3,4,5+1.j])
for norm in ['biased', 'unbiased', 'coeff',None]:
corr1,l = xcorr(x, x, maxlags=4, norm=norm)
corr2 = CORRELATION(x, x, maxlags=4, norm=norm)
assert_array_almost_equal(corr1, func(corr2))
if __name__ == '__main__':
Speech = Speech("bluesky3.wav")
x, Fs =Speech.audioread(8000)
# x = x / np.max(np.abs(x))
inc = 80 # frame shift
wlen = 200 # frame length
win = np.hanning(wlen) # window function
N = len(x) # data length
X = Speech.enframe(x, list(win), inc).T
fn = X.shape[1] # frame number
time = [i / Fs for i in range(N)]
R = np.zeros(X.shape)
for i in range(fn):
u = X[:, i]
R0, _ = xcorr(u)
R[:, i] = R0[wlen - 1 : 2 * wlen - 1]
i = input("Which frame do you want to plot")
i = int(i)
frameTime = Speech.FrameTime(fn, wlen, inc, Fs)
fig = plt.figure(figsize=(9, 16))
plt.subplot(3, 1, 1)
plt.plot(time, x)
plt.xlabel('Time/s')
plt.ylabel('Amplitude')
plt.title('Speech Waveform')
