A.K.03/student_code/my_baby.py

145 lines
3.7 KiB
Python

import numpy as np # For convolution function
import matplotlib.pyplot as plt # For plotting purposes
import scipy
# import samplerate
from scipy import signal
from scipy.optimize import least_squares
from scipy.io import wavfile # For reading .wav files
from scipy.signal import find_peaks # For filter function
from scipy.fft import fft, ifft # For fft and ifft
# def wavaudioread(filename, fs):
# fs_wav, y_wav = wavfile.read(filename)
# y = samplerate.resample(y_wav, fs / fs_wav, "sinc_best")
#
# return y
# ch3 function
def ch3(x,y,Lhat,epsi):
# your code here (read the above text carefully for all the steps):
N = max(len(x), len(y)) + 50
x = np.concatenate((x, np.zeros(N - len(x)))) # Zero padding
y = np.concatenate((y, np.zeros(N - len(y))))
X = fft(x) # FFTs to find X[k] and Y[k]
Y = fft(y)
H = Y / X # Computation of H[k]
threshold = epsi * max(np.absolute(X))
ii = np.absolute(X) < threshold
H[ii] = 0
h = np.real(ifft(H)) # IFFT to find h[n]
h[np.abs(h) < 1e-12] = 0
# Truncation to length Lhat (optional and actually not recommended before you inspect the entire h)
h = h[0:Lhat]
return h
def signal_Crop(y1):
scale = 300
pk = np.argmax(y1)
start = int(max(pk - scale,0))
end = int(pk + scale)
return start, end
def sig_comp(h1,Fs):
h1_peak, _ = find_peaks(h1, height = np.max(h1)*0.99)
peak_1 = h1_peak[0]
sample_range = np.array([peak_1])
time_dif = sample_range / Fs
distance = abs(time_dif) * 34300
return distance, peak_1
def distance_calc(y1,y2,epsi,Fs):
start1, end1 = signal_Crop(y1)
start2, end2 = signal_Crop(y2)
start_true= min(start1, start2)
end_true = max(end1, end2)
y1_crop = y1[start_true:end_true]
y2_crop = y2[start_true:end_true]
Lhat = len(y1_crop)
h1 = ch3(y1_crop, y2_crop, Lhat, epsi)
distance, peak_1 = sig_comp(h1, Fs)
print(distance)
return distance[0]
def location_calc(mic_coords, ref_index, distances, start_point = [230,230,0]):
ref = mic_coords[ref_index]
other_indices = [i for i in range(mic_coords.shape[0]) if i != ref_index]
def residuals(S):
S = np.array([S[0],S[1],0])
res = []
for i, idx in enumerate(other_indices):
mic = mic_coords[idx]
diff = np.linalg.norm(S-mic) - np.linalg.norm(S-ref) - distances[i]
res.append(diff)
return res
sol = least_squares(residuals, start_point)
return sol.x
def location(y,Fs):
y1 = y[0]
y2 = y[1]
y3 = y[2]
y4 = y[3]
y5 = y[4]
epsi = 0.01
d1 = distance_calc(y5,y1,epsi,Fs)
d2 = distance_calc(y5,y2,epsi,Fs)
d3 = distance_calc(y5,y3,epsi,Fs)
d4 = distance_calc(y5,y4,epsi,Fs)
mic_coords = np.array([
[0,0,25], # mic 1
[0,460,25], # mic 2
[460,460,25], # mic 3
[460,0,25], # mic 4
[0,230,55] # mic 5
])
ref_index = 4 #used to makes mic 5 the ref
distances = np.array([d1,d2,d3,d4])
source_pos = location_calc(mic_coords, ref_index, distances)
return source_pos
ref_index = 4 #used to makes mic 5 the ref
distances = np.array([d1,d2,d3,d4])
source_pos = location_calc(mic_coords, ref_index, distances)
return source_pos
if __name__ == "__main__":
# Coordinates of the recordings
record_x = [64, 82, 109, 143, 150, 178, 232]
record_y = [40, 399, 76, 296, 185, 439, 275]
# List to store filenames
filenames = []
# Generate filenames based on coordinates
for i in range(len(record_x)):
real_x = record_x[i]
real_y = record_y[i]
filenames.append(f"../files/Student Recordings/record_x{real_x}_y{real_y}.wav")
# Load the first recording
Fs, recording = wavfile.read(filenames[0])
print(location(recording, Fs))
print(Fs)