diff --git a/process b/process
index e6aa8a7..50a2d3f 100755
--- a/process
+++ b/process
@@ -41,7 +41,10 @@ def process_recording(filename):
     current_event = None
 
     # read blocks of audio data with overlap from sound variable
-    for block_num, block in enumerate(soundfile.blocks(path, blocksize=samples_per_block, overlap=overlapping_samples)):
+    block_num = 0
+    for block in soundfile.blocks(path, blocksize=samples_per_block, overlap=overlapping_samples):
+        block_num += 1
+
         # get block date and calculate FFT
         block_date = recording_date + datetime.timedelta(seconds=block_num * (samples_per_block - overlapping_samples) / samplerate)
         labels = rfftfreq(len(block), d=1/samplerate)
@@ -49,15 +52,16 @@ def process_recording(filename):
         amplitudes = np.abs(complex_amplitudes)
 
         # get amplitudes only between 100 and 1000 Hz
-        adjacent_amplitudes = amplitudes[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
-        adjacent_labels = labels[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
+        search_amplitudes = amplitudes[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
+        search_labels = labels[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
 
         # get the frequency with the highest amplitude
-        max_amplitude = max(adjacent_amplitudes)
-        max_amplitude_index = np.argmax(adjacent_amplitudes)
-        max_freq = adjacent_labels[max_amplitude_index]
+        max_amplitude = max(search_amplitudes)
+        max_amplitude_index = np.argmax(search_amplitudes)
+        max_freq = search_labels[max_amplitude_index]
 
-        # get the average amplitude of the adjacent frequencies
+        # get the average amplitude of the search frequencies
+        adjacent_amplitudes = search_amplitudes[(search_labels < DETECT_FREQUENCY_FROM) | (search_labels > DETECT_FREQUENCY_TO)]
         noise = np.mean(adjacent_amplitudes)/max_amplitude
 
         # check for detection criteria
@@ -97,7 +101,7 @@ def process_recording(filename):
                 write_plot()
 
                 current_event = None
-                block_num += DETECTION_DISTANCE // BLOCK_SECONDS
+                block_num += (DETECTION_DISTANCE // BLOCK_SECONDS) * samples_per_block
 
         block_num += 1
 
@@ -114,7 +118,7 @@ def main():
     os.makedirs(RECORDINGS_DIR, exist_ok=True)
     os.makedirs(PROCESSED_RECORDINGS_DIR, exist_ok=True)
 
-    for filename in os.listdir(RECORDINGS_DIR):
+    for filename in sorted(os.listdir(RECORDINGS_DIR)):
         if filename.endswith(".flac"):
             process_recording(filename)