Merge branch 'Module3' into 'main'

Module 3

See merge request ee2l1/2025-2026/A.K.03!4

student_code/my_baby.py

@@ -0,0 +1,144 @@
+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)
+