Cleaned up localization code 2

student_code/my_firstborn.py

@@ -1,69 +1,126 @@
-import numpy as np                          # For convolution function
+import numpy as np                          # For working with numpy arrays
 import matplotlib.pyplot as plt             # For plotting for tests
 
 from scipy.io import wavfile                # For reading .wav files for testing
-from scipy.signal import find_peaks         # For filter function
+from scipy.signal import find_peaks         # For cropping the microphone recordings
-from scipy.fft import fft, ifft             # For fft and ifft
+from scipy.fft import fft, ifft             # For channel estimation
 from scipy.optimize import least_squares    # For estimating KITT's location
 
 def recording_crop_normalize(recordings, ref_mic):
     # Finding the last peak in the recording of the chosen reference microphone
     ref_sig = recordings[:,ref_mic]
-    ref_peaks = find_peaks(ref_sig, height= 0.5*np.max(ref_sig))
+    ref_peaks, _ = find_peaks(ref_sig, height= 0.5*max(abs(ref_sig)))
     ref_peak = ref_peaks[-1]
 
     # Cropping all recordings to show only the peaks around the reference peak
     start = ref_peak - 1500
     end = ref_peak + 1500
-    recordings_cropped = recordings[start:end]
+    recordings = recordings[start:end]
 
     # Normalizing all recordings after they are cropped
-    amplitude, sample, mic = recordings_cropped.shape
+    samples, mic = recordings.shape
-    recordings_cropped_normalized = np.zeros((amplitude, sample, mic))
+    recordings_cropped_normalized = np.zeros((samples, mic))
     for i in range(mic):
-        recordings_cropped_normalized[:, i] = recordings_cropped[:, i]/max(recordings_cropped[:, i])
+        recordings_cropped_normalized[:, i] = recordings[:, i]/max(abs(recordings[:, i]))
-    return recordings_cropped_normalized
+    recordings = recordings_cropped_normalized
+    return recordings
 
-def ch3(x, y, epsilon):
+def channel_estimation(recording, reference_recording, epsilon):
-    # Find both x (recording) and y (reference recording) in the frequency domain
+    # Finding both the recording and the reference recording in the frequency domain
-    padded_length = max(len(x), len(y))
+    padded_length = max(len(recording), len(reference_recording))
-    X = fft(x, padded_length-len(x))
+    rec_freq = fft(recording, padded_length-len(recording))
-    Y = fft(y, padded_length-len(y))
+    ref_rec_freq = fft(reference_recording, padded_length-len(reference_recording))
 
-    # Perform the deconvolution in the frequency domain
+    # Performing the deconvolution in the frequency domain
-    H = (Y*np.conj(X))/(np.abs(X)**2+epsilon)
+    ch_est_freq = (ref_rec_freq*np.conj(rec_freq))/(np.abs(rec_freq)**2+epsilon)
 
-    # Find the channel estimation the time domain and centres it
+    # Finding the channel estimation in the time domain and centre it
-    channel_estimate = np.real(ifft(H))
+    channel_estimate = np.real(ifft(ch_est_freq))
     channel_estimate = np.fft.fftshift(channel_estimate)
     return channel_estimate
 
-def distance_calc(channel_estimate, sample_frequency):
+def distance_calc(channel_estimate, sampling_rate):
     # Finding the location of the peak in the channel estimate relative to the reference peak
     center = len(channel_estimate)//2
-    peak = np.argmax(np.abs(channel_estimate))
+    peak = np.argmax(abs(channel_estimate))
     sample_range = peak - center
 
-    # Calculating the Time Difference of Arrival (TDOA) from found peak location and using it together with the speed of sound to calculate the distance
+    # Calculating the distance using the Time Difference of Arrival (TDOA) from found peak location
-    time_dif = sample_range / sample_frequency
+    time_dif = sample_range/sampling_rate
-    distance = time_dif * 34300  # cm
+    distance = time_dif * 34300 # cm
     return distance
 
-def location_estimation(mic_coords, ref_mic, distances, start_point = [230,230,0]):
+def location_estimation(mic_locations, ref_mic, distances, start_point = None):
-    # Using the location of the reference microphone as the refence point
+    # Choose a start point which serves as your starting "guessed" location. If no start point is given, choose the middle of the field as the start point.
-    ref_point = mic_coords[ref_mic]
+    if start_point is None:
-    other_indices = [i for i in range(mic_coords.shape[0]) if i != ref_mic]
+        start_point = [230,230,0]
 
-    # Generating the residuals function that is to be minimized
+    # Using the location of the reference microphone as the refence point
-    def residuals_function(point):
+    ref_point = mic_locations[ref_mic]
-        point = np.array([point[0],point[1],0])
+    other_indices = [i for i in range(mic_locations.shape[0]) if i != ref_mic]
+
+    # Generating the residuals function that is to be minimized. This residual is the difference between the "guessed" location and the location calculated from the microphone recordings
+    def residuals_function(guess):
+        guess = np.array([guess[0],guess[1],0])
         residuals = []
         for i, idx in enumerate(other_indices):
-          mic = mic_coords[idx]
+          mic = mic_locations[idx]
-          residual = (np.linalg.norm(point-mic) - np.linalg.norm(point-ref_point)) - distances[i]
+          residual = (np.linalg.norm(guess-mic) - np.linalg.norm(guess-ref_point)) - distances[i]
           residuals.append(residual)
         return residuals
 
-    # Uses the least squares method to minimize the difference between the estimated location and the location calculated from the microphone recordings.
+    # Using the least squares method to minimize the residuals function
     location = least_squares(residuals_function, start_point, bounds = ([0,0,-1],[460,460,1]))
-    return location.x
+    return location.x
+
+def localization(recordings, sampling_rate):
+    # Choosing a reference microphone. 0 is mic 1; 4 is mic 5
+    ref_mic = 4
+
+    # Normalize and crop the recordings
+    recordings = recording_crop_normalize(recordings, ref_mic)
+
+    # Finding the channel estimates between each recording and the reference recording
+    epsilon = 0.0001
+    channel_estimates = []
+    recording, mic = recordings.shape
+    for i in range(mic):
+        if i == ref_mic:
+            continue
+        else:
+            channel_estimates.append(channel_estimation(recordings[:, i], recordings[:, ref_mic], epsilon))
+
+    # Finding the distances that correspond to the Time Difference of Arrival (TDOA) for each channel estimate
+    distances = []
+    for i in range(len(channel_estimates)):
+        distances.append(distance_calc(channel_estimates[i], sampling_rate))
+
+    # Estimating the location using the least squares method
+    mic_locations = np.array([
+        [0, 0, 25],  # mic 1 cm
+        [0, 460, 25],  # mic 2 cm
+        [460, 460, 25],  # mic 3 cm
+        [460, 0, 25],  # mic 4 cm
+        [0, 230, 55]  # mic 5 cm
+    ])
+    location_estimate = location_estimation(mic_locations, ref_mic, distances)
+    return location_estimate
+
+# Test
+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]
+
+    # Generating the filenames
+    filenames = []
+    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")
+
+    for i in range(len(filenames)):
+        sampling_rate, recordings = wavfile.read(filenames[i])
+        print(f"\nRecording {i+1}: {filenames[i]}")
+        location_estimate = localization(recordings, sampling_rate)
+        print("Estimated source position:", location_estimate)