def broadband_performance_dict(self):
'''
Compute the preformace value for an adaptive array.
Return
------
A dict contains 'array gain',
'noise reduction factor',
'signal reduction facor',
'signal distortion index' values
'''
#数字信号宽带计算频率，计算自适应Beamformer的一些性质（宽带的）
freq = np.linspace(-0.5, 0.5, num=1000,
endpoint=True) * 1 / self.sound_field.Ts
freq = freq[1:-1]
# 初始化一些矩阵
filter_vec = np.zeros((self.M, len(freq)), dtype=np.complex) #滤波器系数
steer_vec = np.zeros((self.M, len(freq)), dtype=np.complex) #导向矢量
observed_cov_vec = np.zeros((self.M, self.M, len(freq)),
dtype=np.complex) #观测矩阵相关系数
noise_cov_vec = np.zeros((self.M, self.M, len(freq)),
dtype=np.complex) #噪声的相关系数矩阵
desired_var_vec = np.zeros(len(freq), dtype=np.complex) #期望信号方差
noise_var_vec = self.sound_field.noise_signal_var * np.ones(
len(freq)) #第一个阵元噪声的方差
#赋值
for i, f in enumerate(freq):
filter_vec[:, i] = self.filter(f).T
steer_vec[:, i] = self.steer_vector(f, self.phi).T
desired_var_vec[i] = self.sound_field.desired_signal_var(f)
observed_cov_vec[:, :, i] = self.sound_field.observed_signal_cov(f)
noise_cov_vec[:, :, i] = self.sound_field.noise_signal_cov(f)
# 输出的信号和噪声的能量
output_signal_energy = desired_var_vec.dot(
np.abs(np.sum(filter_vec.conj() * steer_vec,
axis=0))**2) #宽带输出信号能量
output_noise_energy = 0
#equations-8
for i, f in enumerate(freq):
output_noise_energy += hermitian(filter_vec[:, i]).dot(
noise_cov_vec[:, :, i]).dot(filter_vec[:, i]) #宽带输出噪声能量
# 属性字典
performance_dict = {'array gain' : dB(output_signal_energy / output_noise_energy, power=True) \
- self.sound_field._iSNR.get('dB_value'),
'noise reduction factor' : dB(np.sum(noise_var_vec) / output_noise_energy, power=True),
'signal reduction factor': dB(np.sum(desired_var_vec) / output_signal_energy, power=True),
'signal distortion index': dB((desired_var_vec.dot(np.abs(np.sum(filter_vec.conj() * \
steer_vec, axis=0) - 1) ** 2)) / np.sum(desired_var_vec), power=True)
}
return performance_dict
def snr_direct_method(self, f):
'''
Estimate the single-channel Wiener gain directly
'''
hw = hermitian(self.steer_vector(f, self.phi)).dot(self.observed_signal_gamma(f) - \
self.isotropic_noise_coherence(f)).dot(self.steer_vector(f, self.phi)) / \
(self.M ** 2 - hermitian(self.steer_vector(f, self.phi)).dot(self.isotropic_noise_coherence(f)).dot(self.steer_vector(f, self.phi)))
return dB(hw)[0][0]
def white_noise_gain(self):
'''
return the white noise gain in TDB
'''
g = self.steer_vector()
h = self.filter
white_noise_gain = h.T.dot(g).dot(g.T).dot(h) / h.T.dot(h)
white_noise_gain_db = dB(np.abs(white_noise_gain), True)
return white_noise_gain_db
def directivity(self):
'''
return the directivity in TDB
'''
g = self.steer_vector()
h = self.filter
int_g = self.diffuse_noise_coherence()
directivity = (h.T.dot(g.dot(g.T)).dot(h)) / (h.T.dot(int_g).dot(h))
directivity_db = dB(np.abs(directivity), True)
return directivity_db
def beam_pattern(self):
"""
绘制波束形成的波束图
"""
filts = self.filter
performace = []
for theta in constants.get('angle_range'):
g_tmp = self.steer_vector(theta)
performace_theta = filts.T.dot(g_tmp).dot(g_tmp.T).dot(filts)
performace.append(performace_theta)
return dB(np.array(performace), True)
def white_noise_gain(self, f):
'''
For fixed beamforming, the WNG equals to 1 / w.conj().T * w
And in delay and sum the w equals to steervector / self.N
Parameters
----------
f: float
frequency
'''
wng = 1 / np.dot(hermitian(self.filter(f)), self.filter(f))
return dB(wng, True)
def front_back_ratio(self):
'''
return the front_back_ratio in TDB
'''
theta_array_0_90 = np.array_split(constants.get('angle_range'), 2)[0]
theta_array_90_180 = np.array_split(constants.get('angle_range'), 2)[1]
filts = self.filter
gamma_0_half_pi = self.diffuse_noise_coherence(theta_array_0_90)
gamma_half_pi_pi = self.diffuse_noise_coherence(theta_array_90_180)
front_back_ratio = (filts.T.dot(gamma_0_half_pi).dot(filts)) / (
filts.T.dot(gamma_half_pi_pi).dot(filts))
return dB(front_back_ratio, True)
def beam_pattern(self, f):
'''
Compute and Plot beampattern response for microphone array
Parameters
----------
f: float
frequency
'''
omega = self.filter(f)
steer_vector = self.steer_vector(f)
response = np.squeeze(dB(np.abs(hermitian(steer_vector).dot(omega))))
return response
def performance(self):
i = self.i_ell
h = self.filter
g = self.steer_vector()
r_n = self.sound_field.noise_signal_correlation()
r_x = self.sound_field.desired_signal_correlation()
oSNR = h.T.dot(g).dot(r_x).dot(g.T).dot(h) / h.T.dot(r_n).dot(h)
oSNR_dB = np.asscalar(dB(oSNR, True))
nr_factor = r_n[0, 0] / (h.T.dot(r_n).dot(h))
sigr_factor = r_x[0, 0] / (h.T.dot(g).dot(r_x).dot(g.T).dot(h))
sigd_index = (
(g.T.dot(h) - i).T.dot(r_x).dot(g.T.dot(h) - i)) / r_x[0, 0]
performance_dict = {
'array gain': (oSNR_dB - self.sound_field._iSNR.get('dB_value')),
'noise reduction factor': np.asscalar(dB(nr_factor, True)),
'signal reduction factor': dB(sigr_factor, True),
'signal distortion index': dB(sigd_index, True)
}
return performance_dict
def directivity(self, f):
'''
Directivity factor of microphone array
note: python sinc function is sinc(pi*x) / pi*x
Parameters
----------
f: float
frequency
'''
noise_cov = self.diffuse_noise_coherence(f)
di = 1 / hermitian(self.filter(f)).dot(noise_cov).dot(self.filter(f))
return dB(di, True)
def snr_complex_method(self, f):
'''
averege method by real and imag decompostion
'''
TmpH1 = (np.real(self.observed_signal_gamma(f)) - self.isotropic_noise_coherence(f)) / \
(np.real(np.dot(self.steer_vector(f, self.phi).reshape(self.M, 1),
self.steer_vector(f, self.phi).conj().reshape(1,self.M))) - self.isotropic_noise_coherence(f))
TmpH2 = np.imag(self.observed_signal_gamma(f)) / np.imag(
np.dot(
self.steer_vector(f, self.phi).reshape(self.M, 1),
self.steer_vector(f, self.phi).conj().reshape(1, self.M)))
[m_mat, n_mat] = np.meshgrid(np.arange(self.M), np.arange(self.M))
idx = np.where((m_mat - n_mat) > 0)
return dB(
np.complex(
np.sum(TmpH1[idx] + TmpH2[idx]) / self.M / (self.M - 1)))
