diff --git a/process_chunks.py b/process_chunks.py
index ab431cb..aac9cc1 100755
--- a/process_chunks.py
+++ b/process_chunks.py
@@ -22,6 +22,17 @@ OVERTONE_FREQ = TARGET_FREQ * 2
 NFFT = 32768
 SKIP_SECONDS = 10
 
+def detect_event(chunk, samplerate):
+    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)
     print(f"🔍 Verarbeite {input_path}...")
@@ -36,22 +47,11 @@ def process_chunk(filename):
     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 - 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
-
     i = 0
     last_event = -skip_samples
     while i + chunk_samples <= len(data):
         chunk = data[i:i+chunk_samples]
-        if i - last_event >= skip_samples and detect_event(chunk):
+        if i - last_event >= skip_samples and detect_event(chunk, samplerate):
             clip_start = max(0, i - padding_before)
             clip_end = min(len(data), i + chunk_samples + padding_after)
             clip = data[clip_start:clip_end]