diff --git a/scripts/selcal-fft.py b/scripts/selcal-fft.py
index 83eb06c..b37d4d4 100755
--- a/scripts/selcal-fft.py
+++ b/scripts/selcal-fft.py
@@ -95,34 +95,41 @@ delta_f = sample_rate / segment_size
 # Determine the bin range for desired frequency range (100 Hz to 2000 Hz)
 high_bin = int(max_freq / delta_f)
 
+seg_off = int(sample_rate * 0.1)
+act_segs = len(filtered_data) // seg_off
+
 # Initialize a 2D array to store DFT results (magnitude spectrum)
 # Only store the bins within the desired frequency range
-dft_results = np.zeros((num_segments, high_bin))
+dft_results = np.zeros((act_segs, high_bin))
 
-for i in range(num_segments):
-    start = i * segment_size
-    end = start + segment_size
-    segment = filtered_data[start:end]
+for i in range(act_segs):
+    end = (i+1) * seg_off
+    start = end - segment_size
 
-    # Step 4: Apply the DFT
-    dft_result = fft(segment)
+    try:
+        segment = filtered_data[start:end]
 
-    magnitudes = np.abs(dft_result)
-    total_energy = np.sum(magnitudes ** 2)
-    normalized_magnitudes = magnitudes / np.sqrt(total_energy)
-    normalized_magnitude = np.mean(normalized_magnitudes)
+        # Step 4: Apply the DFT
+        dft_result = fft(segment)
 
-    # Store the magnitude spectrum in the 2D array, only for the desired frequency range
-    dft_results[i, :] = normalized_magnitudes[:high_bin]
+        magnitudes = np.abs(dft_result)
+        total_energy = np.sum(magnitudes ** 2)
+        normalized_magnitudes = magnitudes / np.sqrt(total_energy)
+        normalized_magnitude = np.mean(normalized_magnitudes)
 
-    scores = [
-        10 * np.log10(np.sum(normalized_magnitudes[int((f-w)/delta_f):int((f+w)/delta_f)]))
-        for f,w in zip(frequencies, widths)
-    ]
+        # Store the magnitude spectrum in the 2D array, only for the desired frequency range
+        dft_results[i, :] = normalized_magnitudes[:high_bin]
 
-    codes = get_largest_two_indices(scores, 3.0)
-    if codes:
-        print([frequencies[code] for code in codes])
+        scores = [
+            10 * np.log10(np.sum(normalized_magnitudes[int((f-w)/delta_f):int((f+w)/delta_f)]))
+            for f,w in zip(frequencies, widths)
+        ]
+
+        codes = get_largest_two_indices(scores, 3.0)
+        if codes:
+            print([frequencies[code] for code in sorted(codes)])
+    except:
+        pass
 
 
 # Step 5: Plot the spectrogram