diff --git a/process_chunk.py b/process_chunk.py
deleted file mode 100755
index b187c9e..0000000
--- a/process_chunk.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python3
-"""
-Erkennt 211 Hz + 422 Hz (Oberton) in WAV-Dateien.
-Speichert WAV + PNG nur bei Erkennung.
-Blockiert Folgetreffer für definierte Zeit (SKIP_SECONDS).
-"""
-
-import numpy as np
-import soundfile as sf
-from scipy.fft import fft, fftfreq
-import matplotlib.pyplot as plt
-import os
-
-# === Konfiguration ===
-FILENAME = "1b.flac"
-TARGET_FREQ = 211
-OCTAVE_FREQ = TARGET_FREQ * 2
-TOLERANCE = 1
-THRESHOLD_BASE = 0.3
-THRESHOLD_OCT = THRESHOLD_BASE / 10
-CHUNK_SECONDS = 2
-CLIP_PADDING_BEFORE = 2
-CLIP_PADDING_AFTER = 8
-SKIP_SECONDS = 10
-OUTDIR = "events"
-
-os.makedirs(OUTDIR, exist_ok=True)
-
-# === WAV/Audio-Datei laden ===
-data, rate = sf.read(FILENAME, dtype='float32')
-if data.ndim > 1:
-    data = data.mean(axis=1)
-
-samples_per_chunk = int(rate * CHUNK_SECONDS)
-total_chunks = len(data) // samples_per_chunk
-
-detections = []
-next_allowed_time = 0  # für Skip-Logik
-
-# === Analyse-Loop ===
-for i in range(total_chunks):
-    timestamp = i * CHUNK_SECONDS
-    if timestamp < next_allowed_time:
-        continue
-
-    segment = data[i * samples_per_chunk : (i + 1) * samples_per_chunk]
-    if len(segment) == 0:
-        continue
-
-    freqs = fftfreq(len(segment), d=1/rate)
-    fft_vals = np.abs(fft(segment))
-
-    pos_mask = freqs > 0
-    freqs = freqs[pos_mask]
-    fft_vals = fft_vals[pos_mask]
-
-    peak_freq = freqs[np.argmax(fft_vals)]
-    peak_mag = np.max(fft_vals)
-
-    # Energien normiert
-    mask_base = (freqs >= TARGET_FREQ - TOLERANCE) & (freqs <= TARGET_FREQ + TOLERANCE)
-    energy_base = np.mean(fft_vals[mask_base]) / peak_mag
-
-    mask_oct = (freqs >= OCTAVE_FREQ - TOLERANCE) & (freqs <= OCTAVE_FREQ + TOLERANCE)
-    energy_oct = np.mean(fft_vals[mask_oct]) / peak_mag
-
-    is_peak_near_target = TARGET_FREQ - TOLERANCE <= peak_freq <= TARGET_FREQ + TOLERANCE
-    detected = is_peak_near_target and energy_base > THRESHOLD_BASE and energy_oct > THRESHOLD_OCT
-
-    if detected:
-        detections.append((timestamp, round(energy_base, 4), round(energy_oct, 4), round(peak_freq, 2)))
-        next_allowed_time = timestamp + SKIP_SECONDS
-
-        # Ausschnitt extrahieren
-        start = max(0, int((timestamp - CLIP_PADDING_BEFORE) * rate))
-        end = min(len(data), int((timestamp + CLIP_PADDING_AFTER) * rate))
-        clip = (data[start:end] * 32767).astype(np.int16)
-
-        base_filename = os.path.join(OUTDIR, f"event_{int(timestamp):04}s")
-        wav_name = f"{base_filename}.wav"
-        png_name = f"{base_filename}.png"
-
-        # WAV speichern
-        sf.write(wav_name, clip, rate, subtype="PCM_24")
-        print(f"🟢 WAV gespeichert: {wav_name} (211Hz: {energy_base:.4f}, 422Hz: {energy_oct:.4f}, Peak: {peak_freq:.1f} Hz)")
-
-        # PNG Spektrogramm
-        plt.figure(figsize=(10, 4))
-        # Verstärke das Signal künstlich, um schwache Ereignisse im dB-Spektrum sichtbarer zu machen
-        plt.specgram((clip / 32767.0), NFFT=32768, Fs=rate, noverlap=512, cmap="plasma", vmin=-80, vmax=-35)
-        plt.title(f"Ereignis @ {timestamp:.2f}s")
-        plt.xlabel("Zeit (s)")
-        plt.ylabel("Frequenz (Hz)")
-        plt.ylim(0, 1000)
-        plt.colorbar(label="Intensität (dB)")
-        plt.tight_layout()
-        plt.savefig(png_name)
-        plt.close()
-        print(f"📷 PNG gespeichert: {png_name}")
-
-# === Zusammenfassung ===
-print("\n🎯 Erkennungen:")
-for ts, eb, eo, pf in detections:
-    print(f"- {ts:.2f}s | 211Hz: {eb} | 422Hz: {eo} | Peak: {pf:.1f} Hz")
-
-if not detections:
-    print("→ Keine gültigen Ereignisse erkannt.")
\ No newline at end of file