diff --git a/process_chunks.py b/process_chunks.py
index c24a526..26c8a9b 100755
--- a/process_chunks.py
+++ b/process_chunks.py
@@ -10,6 +10,28 @@ import shutil
 
 OUTDIR = "chunks_unprocessed"
 PROCESSED_DIR = "chunks_processed"
+CHUNK_SECONDS = 1
+TOLERANCE = 1
+OVERTONE_TOLERANCE = TOLERANCE * 2
+THRESHOLD_BASE = 0.5
+THRESHOLD_OCT = THRESHOLD_BASE / 10
+CLIP_PADDING_BEFORE = 1
+CLIP_PADDING_AFTER = 6
+TARGET_FREQ = 211
+OVERTONE_FREQ = TARGET_FREQ * 2
+NFFT = 32768
+SKIP_SECONDS = 10
+
+def detect_event(chunk):
+    freqs, times, Sxx = scipy.signal.spectrogram(chunk, samplerate, nperseg=NFFT)
+    idx_base = np.where((freqs >= TARGET_FREQ - TOLERANCE) & (freqs <= TARGET_FREQ + TOLERANCE))[0]
+    idx_oct = np.where((freqs >= OVERTONE_FREQ - OVERTONE_TOLERANCE) & (freqs <= OVERTONE_FREQ + OVERTONE_TOLERANCE))[0]
+    if len(idx_base) == 0 or len(idx_oct) == 0:
+        return False
+    base_energy = np.mean(Sxx[idx_base])
+    oct_energy = np.mean(Sxx[idx_oct])
+    total_energy = np.mean(Sxx, axis=0).max()
+    return base_energy > THRESHOLD_BASE * total_energy and oct_energy > THRESHOLD_OCT * total_energy
 
 def process_chunk(filename):
     input_path = os.path.join(OUTDIR, filename)
@@ -20,33 +42,11 @@ def process_chunk(filename):
     if data.ndim > 1:
         data = data[:, 0]  # nur Kanal 1
 
-    CHUNK_SECONDS = 1
-    TOLERANCE = 1
-    THRESHOLD_BASE = 0.5
-    THRESHOLD_OCT = THRESHOLD_BASE / 10
-    CLIP_PADDING_BEFORE = 1
-    CLIP_PADDING_AFTER = 6
-    TARGET_FREQ = 211
-    OVERTONE_FREQ = TARGET_FREQ * 2
-    NFFT = 32768
-    SKIP_SECONDS = 10
-
     chunk_samples = int(CHUNK_SECONDS * samplerate)
     skip_samples = int(SKIP_SECONDS * samplerate)
     padding_before = int(CLIP_PADDING_BEFORE * samplerate)
     padding_after = int(CLIP_PADDING_AFTER * samplerate)
 
-    def detect_event(chunk):
-        freqs, times, Sxx = scipy.signal.spectrogram(chunk, samplerate, nperseg=NFFT)
-        idx_base = np.where((freqs >= TARGET_FREQ - TOLERANCE) & (freqs <= TARGET_FREQ + TOLERANCE))[0]
-        idx_oct = np.where((freqs >= OVERTONE_FREQ - TOLERANCE) & (freqs <= OVERTONE_FREQ + TOLERANCE))[0]
-        if len(idx_base) == 0 or len(idx_oct) == 0:
-            return False
-        base_energy = np.mean(Sxx[idx_base])
-        oct_energy = np.mean(Sxx[idx_oct])
-        total_energy = np.mean(Sxx, axis=0).max()
-        return base_energy > THRESHOLD_BASE * total_energy and oct_energy > THRESHOLD_OCT * total_energy
-
     i = 0
     last_event = -skip_samples
     while i + chunk_samples <= len(data):