diff --git a/process b/process
index 9a1e5a9..3df49a8 100755
--- a/process
+++ b/process
@@ -21,7 +21,7 @@ ADJACENCY_FACTOR = 2 # area to look for noise around the target frequency
 AMPLITUDE_THRESHOLD = 200 # relative DB (rDB) (because not calibrated)
 BLOCK_SECONDS = 3 # seconds (longer means more frequency resolution, but less time resolution)
 DETECTION_DISTANCE = 30 # seconds (minimum time between detections)
-BLOCK_OVERLAP = 0.8 # overlap between blocks (0.8 means 80% overlap)
+BLOCK_OVERLAP_FACTOR = 0.8 # overlap between blocks (0.8 means 80% overlap)
 
 
 def process_recording(filename):
@@ -35,7 +35,7 @@ def process_recording(filename):
     path = os.path.join(RECORDINGS_DIR, filename)
     sound, samplerate = soundfile.read(path)
     samples_per_block = int(BLOCK_SECONDS * samplerate)
-    overlap = int(samples_per_block * BLOCK_OVERLAP)
+    overlapping_samples = int(samples_per_block * BLOCK_OVERLAP_FACTOR)
 
     # get median amplitude for normalization
     complex_amplitudes_global = rfft(sound)
@@ -47,12 +47,12 @@ def process_recording(filename):
     is_detecting = False
 
     # read blocks of audio data with overlap from sound variable
-    for num in range(0, len(sound) // (samples_per_block - overlap)):
-        start = num * (samples_per_block - overlap)
-        end = start + samples_per_block
-        block = sound[start:end]
+    for block_num in range(0, len(sound) // (samples_per_block - overlapping_samples)):
+        start_sample = block_num * (samples_per_block - overlapping_samples)
+        end_sample = start_sample + samples_per_block
+        block = sound[start_sample:end_sample]
 
-        block_date = recording_date + datetime.timedelta(seconds=num * BLOCK_SECONDS)
+        block_date = recording_date + datetime.timedelta(seconds=block_num * BLOCK_SECONDS)
         labels = rfftfreq(len(block), d=1/samplerate)
         complex_amplitudes = rfft(block)
         absolute_amplitudes = np.abs(complex_amplitudes)