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ambisonics.py
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ambisonics.py
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import green_function as gr
import function as fu
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.cm as cm
#初期条件
ambi_order = 7 #打ち切り次数
freq = 550 #周波数
pos_s = np.array([0.1,0,0]) #一次音源の座標x,y,z
cv = 344 #音速
r_sp = 0.5 #スピーカーを配置する半径
r_mic = 1.0 #マイクロホンの半径
#グラフ条件
Plot_range = 2.5
space = 0.02
#計算
lambda_ = cv/freq
omega = 2*np.pi*freq
k = omega/cv
#スピーカー配置を決定
num_sp = (ambi_order+1)**2
xyz = fu.EquiDistSphere((ambi_order+1)**2)
xyz = np.array([xyz]).reshape(num_sp,3)
pos_sp = xyz * r_sp
azi_sp, ele_sp, rr_sp = fu.cart2sph(pos_sp[:,0],pos_sp[:,1],pos_sp[:,2])
ele_sp1 = np.pi /2 - ele_sp
#マイク配置を決定
num_mic = (ambi_order+1)**2
pos_mic = xyz * r_mic
azi_mic, ele_mic, rr_mic = fu.cart2sph(pos_mic[:,0],pos_mic[:,1],pos_mic[:,2])
ele_mic1 = np.pi /2 - ele_mic
mic_sig = np.array([])
for i in range(num_mic):
mic_sig_ = gr.green2(1,freq,cv,pos_s,pos_mic[i])
mic_sig = np.append(mic_sig, mic_sig_)
#エンコード
ambi_signal = fu.encode(ambi_order,azi_mic,ele_mic1,k,r_mic,np.linalg.norm(pos_s, ord=2),mic_sig)
#デコード
spkr_output = fu.decode(ambi_order,azi_sp,ele_sp1,k,r_mic,r_sp,np.linalg.norm(pos_s, ord=2),lambda_,ambi_signal)
#所望音場(一次音源)と再現音場を求める
X = np.arange(-Plot_range,Plot_range+space,space)
Y = np.arange(-Plot_range,Plot_range+space,space)
XX,YY = np.meshgrid(X,Y)
P_org = np.zeros((X.size,Y.size), dtype = np.complex)
P_HOA = np.zeros((X.size,Y.size), dtype = np.complex)
for j in range(X.size):
for i in range(Y.size):
pos_r = np.array([X[j],Y[i],0])
P_org[i,j] = gr.green2(1,freq,cv,pos_s,pos_r)
for l in range(num_sp):
G_transfer = gr.green2(1,freq,cv,pos_sp[l,:],pos_r)
P_HOA[i,j] += G_transfer * spkr_output[l]
NE = np.zeros((X.size,Y.size), dtype = np.complex)
for i in range(X.size):
for j in range(Y.size):
NE[i,j] = 10*np.log10(((np.abs(P_HOA[i,j]-P_org[i,j]))**2)/((np.abs(P_org[i,j]))**2))
#グラフ
#所望音場
r_sp_ = patches.Circle(xy=(0, 0), radius=r_sp, ec='b',fill=False)
plt.figure()
ax = plt.axes()
im = ax.imshow(np.real(P_org), interpolation='gaussian',cmap=cm.jet,origin='lower',
extent=[-Plot_range, Plot_range, -Plot_range, Plot_range],
vmax=0.1, vmin=-0.1)
ax.grid(False)
ax.add_patch(r_sp_)
plt.axis('scaled')
ax.set_aspect('equal')
plt.colorbar(im)
plt.savefig('original_sound.pdf',bbox_inches="tight",dpi = 64,
facecolor = "lightgray", tight_layout = True)
plt.show()
#再現音場
r_sp_ = patches.Circle(xy=(0, 0), radius=r_sp, ec='b',fill=False)
plt.figure()
ax2 = plt.axes()
im2 = ax2.imshow(np.real(P_HOA), interpolation='gaussian',cmap=cm.jet,origin='lower',
extent=[-Plot_range, Plot_range, -Plot_range, Plot_range],
vmax=0.1, vmin=-0.1)
ax2.grid(False)
ax2.add_patch(r_sp_)
plt.axis('scaled')
ax2.set_aspect('equal')
plt.colorbar(im2)
plt.savefig('reproduced_sound.pdf',bbox_inches="tight",dpi = 64,
facecolor = "lightgray", tight_layout = True)
plt.show()
#正規化誤差
r_sp_ = patches.Circle(xy=(0, 0), radius=r_sp, ec='b',fill=False)
plt.figure()
ax3 = plt.axes()
im3 = ax3.imshow(np.real(NE), interpolation='gaussian',cmap=cm.pink_r,origin='lower',
extent=[-Plot_range, Plot_range, -Plot_range, Plot_range],
vmax=0, vmin=-60)
ax3.grid(False)
ax3.add_patch(r_sp_)
plt.axis('scaled')
ax3.set_aspect('equal')
plt.colorbar(im3)
plt.savefig('normalization_error.pdf',bbox_inches="tight",dpi = 64,
facecolor = "lightgray", tight_layout = True)
plt.show()