cronekorkn/bootshorn_investigation
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CroneKorkN 2025-06-01 19:49:19 +02:00
parent d804d781c6
commit 407a5228bf
Signed by: cronekorkn
SSH key fingerprint: SHA256:v0410ZKfuO1QHdgKBsdQNF64xmTxOF8osF1LIqwTcVw
1 changed files with 24 additions and 19 deletions
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43
process
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@ -3,7 +3,7 @@ import os
import datetime
import numpy as np
import matplotlib.pyplot as plt
import soundfile
import soundfile as sf
from scipy.fft import rfft, rfftfreq
import shutil
import traceback
@ -36,20 +36,20 @@ def process_recording(filename):
# get data and metadata from recording
path = os.path.join(RECORDINGS_DIR, filename)
sound, samplerate = soundfile.read(path)
soundfile = sf.SoundFile(path)
samplerate = soundfile.samplerate
samples_per_block = int(BLOCK_SECONDS * samplerate)
overlapping_samples = int(samples_per_block * BLOCK_OVERLAP_FACTOR)
# chache data about current event
sample_num = 0
current_event = None
# read blocks of audio data with overlap from sound variable
sample_num = 0
while sample_num < len(sound):
# get block of audio data
block_start = sample_num
block_end = min(sample_num + samples_per_block, len(sound))
block = sound[block_start:block_end]
while sample_num < len(soundfile):
soundfile.seek(sample_num)
block = soundfile.read(frames=samples_per_block, dtype='float32', always_2d=False)
if len(block) == 0:
break
# calculate FFT
labels = rfftfreq(len(block), d=1/samplerate)
@ -102,7 +102,8 @@ def process_recording(filename):
current_event['duration'] = duration
print(f'🔊 {current_event['start_at'].strftime('%Y-%m-%d %H:%M:%S')} ({duration:.1f}s): {current_event['start_freq']:.1f}Hz->{current_event['end_freq']:.1f}Hz @{current_event['max_amplitude']:.0f}rDB')
write_event(current_event=current_event, sound=sound, samplerate=samplerate)
# read full audio clip again for writing
write_event(current_event=current_event, soundfile=soundfile, samplerate=samplerate)
current_event = None
sample_num += DETECTION_DISTANCE_BLOCKS * samples_per_block
@ -111,27 +112,31 @@ def process_recording(filename):
# write a spectrogram using the sound from start to end of the event
def write_event(current_event, sound, samplerate):
def write_event(current_event, soundfile, samplerate):
# date and filename
event_date = current_event['start_at'] - datetime.timedelta(seconds=PLOT_PADDING_START_SECONDS)
filename_prefix = event_date.strftime('%Y-%m-%d_%H-%M-%S.%f%z')
# event clip
event_start_sample = current_event['start_sample'] - samplerate * PLOT_PADDING_START_SECONDS
event_end_sample = current_event['end_sample'] + samplerate * PLOT_PADDING_END_SECONDS
event_clip = sound[event_start_sample:event_end_sample]
event = current_event['start_at'] - datetime.timedelta(seconds=PLOT_PADDING_START_SECONDS)
filename_prefix = current_event['start_at'].strftime('%Y-%m-%d_%H-%M-%S.%f%z')
total_samples = event_end_sample - event_start_sample
soundfile.seek(event_start_sample)
event_clip = soundfile.read(frames=total_samples, dtype='float32', always_2d=False)
# write flac
flac_path = os.path.join(DETECTIONS_DIR, f"{filename_prefix}.flac")
soundfile.write(flac_path, event_clip, samplerate, format='FLAC')
sf.write(flac_path, event_clip, samplerate, format='FLAC')
# write spectrogram
plt.figure(figsize=(8, 6))
plt.specgram(event_clip, Fs=samplerate, NFFT=samplerate, noverlap=samplerate//2, cmap='inferno', vmin=-100, vmax=-10)
plt.title(f"Bootshorn @{event.strftime('%Y-%m-%d %H:%M:%S%z')}")
plt.title(f"Bootshorn @{event_date.strftime('%Y-%m-%d %H:%M:%S%z')}")
plt.xlabel(f"Time {current_event['duration']:.1f}s")
plt.ylabel(f"Frequency {current_event['start_freq']:.1f}Hz -> {current_event['end_freq']:.1f}Hz")
plt.colorbar(label="Intensity (rDB)")
plt.ylim(50, 1000)
spectrogram_path = os.path.join(DETECTIONS_DIR, f"{filename_prefix}.png")
plt.savefig(spectrogram_path)
plt.savefig(os.path.join(DETECTIONS_DIR, f"{filename_prefix}.png"))
plt.close()