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