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Avg_System_distance_MISO.py
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# -*- coding: utf-8 -*-
@author: davidkumar
""
import numpy as np
import scipy.signal as sig
import matplotlib.pyplot as plt
from scipy.interpolate import Rbf, InterpolatedUnivariateSpline
from scipy import interpolate
from utility import *
def initialize(Q, fs, Lf, xs):
Omega = 2 * np.pi / Q # angular speed of the microphone [rad/s]
L = int(2 * np.pi / Omega * fs)
t = (1 / fs) * np.arange(L)
phi = Omega * t
distance = np.sqrt((R*np.cos(phi)-xs[0])**2 + (R*np.sin(phi)-xs[1])**2)
delay = distance / c
weight = 1 / distance
type = 'lagrange' # FD filters
waveform, shift, offset = fractional_delay(delay, Lf, fs=fs, type=type) # getting impulse_respones
waveform = waveform * weight[:, np.newaxis]
#h, _, _ = construct_ir_matrix(waveform*weight[:, np.newaxis], shift, N)
# getting captured signal for each microphone
s = captured_signal(waveform, shift, p)
return s, phi
def calc_impulse_response(K, N, s, phi, Phi, interp_method, h, p):
# calculating of impulse_response
impulse_response = np.zeros((N, K))
for k in range(K):
y = np.zeros(N)
for i in range(N):
s_i_linear = s[i::N]
phi_i_linear = phi[i::N] # Decompose the captured signal into N sub-signals
y[i] = spatial_interpolation(s_i_linear, phi_i_linear, Phi[k], interp_method) # interpolation
impulse_response[:, k] = cxcorr(y, p)
DD = np.zeros(K)
for psi in range(K):
nummer = numerator(impulse_response[:, psi], h[:, psi])# numerator of formula
denom = denominator(h[:, psi])
DD[psi] = nummer/denom
return DD, impulse_response
##########################################################################################
# Constants
c = 343 # speed of sound [m/s]
fs = 8000 # sampling frequency [Hz]
# Parameters
N = 150 # length of the impulse response
K = 90 # desired number of impulse responses
Lf = 13 # length of the fractional delay filter
inter_method = 4
# Q = [0.628, 1.375, 6.28] #12
m_omega = 10
Q = np.linspace(6.28, 0.628, num=m_omega, endpoint=False) #12
# Source position
num_source = 2
#xs = [0, 2]
xs = [[0, 2], [0, -2]]
D = np.zeros((m_omega, inter_method, K))
Avg_D = np.zeros((inter_method, m_omega))
# Receiver positions on a circle
R = 0.5 # radius
Phi = np.linspace(0, 2*np.pi, num=K, endpoint=False)
distance = np.sqrt((R*np.cos(Phi)-xs[0][0])**2 + (R*np.sin(Phi)-xs[0][1])**2)
delay = distance / c
weight = 1 / distance
#######################Static impulse respones########################
waveform, shift, _ = fractional_delay(delay, Lf, fs=fs, type='lagrange')
h, _, _ = construct_ir_matrix(waveform*weight[:, np.newaxis], shift, N)
h = h.T
#denom = denominator(h, Phi)# denominator of formula
#######################End of Static response######################
# Excitation by perfet sequences.
# p = perfect_sequence_randomphase(N)
p = perfect_sweep(N)
for ii in range(len(Q)):
s_0, phi = initialize(Q[ii], fs, Lf, xs[0])
s_1, _ = initialize(Q[ii], fs, Lf, xs[1])
s = s_0 + s_1
impulse_response = np.zeros((N, K))
#####################################Interpolation method is linear#####################################################
interp_method = 'linear'
D[ii, 0, :], _ = calc_impulse_response(K, N, s, phi, Phi, interp_method, h, p)
Avg_D[0, ii] = db(average_fwai(D[ii, 0, :]))
#####################################Interpolation method is nearestNeighbour#####################################################
interp_method = 'nearestNeighbour'
D[ii, 1, :], _ = calc_impulse_response(K, N, s, phi, Phi, interp_method, h, p)
Avg_D[1, ii] = db(average_fwai(D[ii, 1, :]))
#####################################Interpolation method is sinc#####################################################
interp_method = 'sinc'
D[ii, 2, :], _ = calc_impulse_response(K, N, s, phi, Phi, interp_method, h, p)
Avg_D[2, ii] = db(average_fwai(D[ii, 2, :]))
#####################################Interpolation method is spline#####################################################
interp_method = 'spline'
D[ii, 3, :], _ = calc_impulse_response(K, N, s, phi, Phi, interp_method, h, p)
Avg_D[3, ii] = db(average_fwai(D[ii, 3, :]))
Omega = 2 * np.pi / Q
#Omega = np.rad2deg(2 * np.pi / Q)
min_o = np.amin(Avg_D)
max_o = np.amax(Avg_D)
#Descret line for Omege_C
y_val = np.linspace(min_o, max_o, num=50)
Qmega_o = 2 * np.pi / 1.375
Omega_seq = np.ones((1, 50)) * Qmega_o
# Plot
plt.figure()
plt.plot(Omega, Avg_D[0, :], label="linear")
plt.plot(Omega, Avg_D[1, :], label="NearestNeighbour")
plt.plot(Omega, Avg_D[2, :], label="Sinc")
plt.plot(Omega, Avg_D[3, :], label="Spline")
plt.plot(Omega_seq[0, :], y_val, label="Omega_C(Q=1.375):{}".format(Qmega_o)+"rad/s")
plt.legend()
plt.grid()
#plt.xlim(0, 360)
#plt.ylim(max_o)
plt.xlabel('Omega : rad/s')
plt.ylabel(r'$Average$ $System$ $distance$ / dB')
plt.title('Average System distance')
plt.show()