diff --git a/student_code/my_firstborn.py b/student_code/my_firstborn.py
index f244daf..f74537c 100644
--- a/student_code/my_firstborn.py
+++ b/student_code/my_firstborn.py
@@ -3,7 +3,7 @@ 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 cropping the microphone recordings
-from scipy.fft import fft, ifft             # For channel estimation
+from scipy.fft import fft, ifft, fftshift   # For channel estimation
 from scipy.optimize import least_squares    # For estimating KITT's location
 
 def recording_crop_normalize(recordings, ref_mic):
@@ -13,8 +13,8 @@ def recording_crop_normalize(recordings, ref_mic):
     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
+    start = ref_peak - 3600
+    end = ref_peak + 3600
     recordings = recordings[start:end]
 
     # Normalizing all recordings after they are cropped
@@ -28,15 +28,15 @@ def recording_crop_normalize(recordings, ref_mic):
 def channel_estimation(recording, reference_recording, epsilon):
     # Finding both the recording and the reference recording in the frequency domain
     padded_length = max(len(recording), len(reference_recording))
-    rec_freq = fft(recording, padded_length-len(recording))
-    ref_rec_freq = fft(reference_recording, padded_length-len(reference_recording))
+    rec_freq = fft(recording, padded_length)
+    ref_rec_freq = fft(reference_recording, padded_length)
 
     # Performing the deconvolution in the frequency domain
     ch_est_freq = (ref_rec_freq*np.conj(rec_freq))/(np.abs(rec_freq)**2+epsilon)
 
     # Finding the channel estimation in the time domain and centre it
-    channel_estimate = np.real(ifft(ch_est_freq))
-    channel_estimate = np.fft.fftshift(channel_estimate)
+    channel_estimate = abs(ifft(ch_est_freq))
+    channel_estimate = fftshift(channel_estimate)
     return channel_estimate
 
 def distance_calc(channel_estimate, sampling_rate):
@@ -57,16 +57,17 @@ def location_estimation(mic_locations, ref_mic, distances, start_point = None):
 
     # Using the location of the reference microphone as the refence point
     ref_point = mic_locations[ref_mic]
-    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_locations[idx]
-          residual = (np.linalg.norm(guess-mic) - np.linalg.norm(guess-ref_point)) - distances[i]
-          residuals.append(residual)
+        mic, axs = mic_locations.shape
+        for i in range(mic):
+            if i != ref_mic:
+                mic_location = mic_locations[i]
+                residual = (np.linalg.norm(guess-mic_location) - np.linalg.norm(guess-ref_point)) + distances[i]
+                residuals.append(residual)
         return residuals
 
     # Using the least squares method to minimize the residuals function
@@ -85,9 +86,7 @@ def localization(recordings, sampling_rate):
     channel_estimates = []
     recording, mic = recordings.shape
     for i in range(mic):
-        if i == ref_mic:
-            continue
-        else:
+        if i != ref_mic:
             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