def idct_2d_ref(x, **kwargs):
""" used as a reference in testing idct2. """
x = np.array(x, copy=True)
for row in range(x.shape[0]):
x[row, :] = idct(x[row, :], **kwargs)
for col in range(x.shape[1]):
x[:, col] = idct(x[:, col], **kwargs)
return x
def idct_2d_ref(x, **kwargs):
"""Calculate reference values for testing idct2."""
x = np.array(x, copy=True)
for row in range(x.shape[0]):
x[row, :] = idct(x[row, :], **kwargs)
for col in range(x.shape[1]):
x[:, col] = idct(x[:, col], **kwargs)
return x
def test_definition(self):
for i in FFTWDATA_SIZES:
xr, yr, dt = fftw_dct_ref(self.type, i, self.rdt)
x = idct(yr, type=self.type)
if self.type == 1:
x /= 2 * (i-1)
else:
x /= 2 * i
assert_equal(x.dtype, dt)
# XXX: we divide by np.max(y) because the tests fail otherwise. We
# should really use something like assert_array_approx_equal. The
# difference is due to fftw using a better algorithm w.r.t error
# propagation compared to the ones from fftpack.
assert_array_almost_equal(x / np.max(x), xr / np.max(x), decimal=self.dec,
err_msg="Size %d failed" % i)
def IDCTcoefficient(sig,
samplerate=16000,
win_length=0.025,
win_step=0.01,
pre_emphasis_coeff=0.97):
'''
计算初始IDCT系数
:param sig:
:param samplerate:
:param win_length:
:param win_step:
:param pre_emphasis_coeff:
:return:
'''
#预处理
signal = pre_emphasis(sig, pre_emphasis_coeff)
#分帧
frames = audio2frame(signal, win_length * samplerate,
win_step * samplerate) # 得到帧数组
#加窗
frames *= np.hamming(int(round(win_length * samplerate))) # 加窗
#DCT
dctfeat = dct(frames, type=2, axis=1, norm='ortho') # 进行离散余弦变换
#为0无法取对数
#dctfeat[dctfeat < 1e-30] = 1e-30
dctfeatnoz = np.where(dctfeat == 0,
np.finfo(float).eps,
dctfeat) #对为0的地方调整为eps，这样便于进行对数处理
dctfeatnoz = np.absolute(dctfeatnoz)
#取对数
logfeat = np.log(dctfeatnoz)
del dctfeatnoz
#取IDCT
idctfeat = idct(logfeat, type=2, axis=1, norm='ortho') # 进行反离散余弦变换
return idctfeat
def test_idct_complex(self):
y = idct(np.arange(5) * 1j)
x = 1j * idct(np.arange(5))
assert_array_almost_equal(x, y)
# plt.subplot(4, 1, 2)
# plt.title("DCT")
# plt.plot(range(feat.shape[1]), feat[0])
# feat=np.power(feat,2)
feat = abs(feat)
feat = np.where(feat == 0, np.finfo(float).eps, feat)
feat = np.log(feat)
# plt.subplot(4, 1, 3)
# plt.title("Log")
# # plt.plot(range(175),feat[0][25:200])
# plt.plot(range(feat.shape[1]), feat[0])
feat = idct(feat)
# plt.subplot(4,1,4)
plt.title("IDCT")
# plt.plot(range(175),feat[0][25:200])
plt.plot(range(feat.shape[1]), abs(feat[0]))
# plt.subplot(4, 1, 3)
# plt.plot(range(feat.shape[1]), feat[0]-np.array( sig[:pointnum]))
plt.show()
# x = np.linspace(-0, 1, 10000)
# a = np.sin(2*np.pi*x)#+np.cos(2*np.pi*x) +np.sin(8*np.pi*x)+np.cos(2*np.pi*x)+np.sin(np.pi*x)+np.sin(6*np.pi*x)
# plt.subplot(4, 1, 1)
# plt.plot(x,a)
#
