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CroneKorkN 2025-05-30 19:17:25 +02:00
parent bf16f740cd
commit 0990821d6d
Signed by: cronekorkn
SSH key fingerprint: SHA256:v0410ZKfuO1QHdgKBsdQNF64xmTxOF8osF1LIqwTcVw
1 changed files with 22 additions and 22 deletions
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44
process_chunks.py
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@ -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):