cronekorkn/bootshorn_investigation
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

wip

Browse source
This commit is contained in:
CroneKorkN 2025-05-30 22:00:57 +02:00
parent 6626c3696d
commit f86cd966b0
Signed by: cronekorkn
SSH key fingerprint: SHA256:v0410ZKfuO1QHdgKBsdQNF64xmTxOF8osF1LIqwTcVw
1 changed files with 6 additions and 5 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

11
process_chunks.py
View file

@ -4,7 +4,7 @@ import concurrent.futures
import datetime import datetime
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import soundfile as sf import soundfile
import scipy.signal import scipy.signal
from scipy.fft import fft, fftfreq from scipy.fft import fft, fftfreq
import shutil import shutil
@ -29,7 +29,7 @@ def process_chunk(filename):
print(f"🔍 Verarbeite {input_path}...") print(f"🔍 Verarbeite {input_path}...")
# Frequenzanalyse und Event-Erkennung # Frequenzanalyse und Event-Erkennung
data, samplerate = sf.read(input_path) data, samplerate = soundfile.read(input_path)
if data.ndim > 1: if data.ndim > 1:
data = data[:, 0] # nur Kanal 1 data = data[:, 0] # nur Kanal 1
@ -43,6 +43,7 @@ def process_chunk(filename):
i = 0 i = 0
last_event = -skip_samples last_event = -skip_samples
while i + chunk_samples <= len(data): while i + chunk_samples <= len(data):
clip = data[i:i+chunk_samples] clip = data[i:i+chunk_samples]
@ -53,7 +54,7 @@ def process_chunk(filename):
return False return False
base_energy = np.mean(Sxx[idx_base]) base_energy = np.mean(Sxx[idx_base])
oct_energy = np.mean(Sxx[idx_oct]) oct_energy = np.mean(Sxx[idx_oct])
total_energy = np.mean(Sxx, axis=0).max() total_energy = np.mean(Sxx[freqs <= 1000, :], axis=0).max()
fft_vals = np.abs(fft(clip)) fft_vals = np.abs(fft(clip))
freqs = fftfreq(len(clip), 1/samplerate) freqs = fftfreq(len(clip), 1/samplerate)
@ -73,7 +74,7 @@ def process_chunk(filename):
flac_out = os.path.join(EVENT_DIR, f"{event_time}.flac") flac_out = os.path.join(EVENT_DIR, f"{event_time}.flac")
png_out = os.path.join(EVENT_DIR, f"{event_time}.png") png_out = os.path.join(EVENT_DIR, f"{event_time}.png")
sf.write(flac_out, clip, samplerate, format='FLAC') soundfile.write(flac_out, clip, samplerate, format='FLAC')
plt.figure() plt.figure()
plt.specgram(clip, Fs=samplerate, NFFT=NFFT, noverlap=NFFT//2, cmap='inferno', vmin=-90, vmax=-20) plt.specgram(clip, Fs=samplerate, NFFT=NFFT, noverlap=NFFT//2, cmap='inferno', vmin=-90, vmax=-20)
@ -84,7 +85,7 @@ def process_chunk(filename):
plt.savefig(png_out) plt.savefig(png_out)
plt.close() plt.close()
print(f"Event: {event_time} peak_freq: {peak_freq:.9f} Hz, base_energy: {base_energy:.9f}, oct_energy: {oct_energy:.9f}, total_energy: {total_energy:.9f}") print(f"Event: {event_time} peak_freq: {peak_freq:.9f} Hz, base_energy: {base_energy/total_energy:.9f}, oct_energy: {oct_energy/total_energy:.9f}, total_energy: {total_energy:.9f}")
last_event = i last_event = i
i += skip_samples i += skip_samples
else: else: