cronekorkn/bootshorn_investigation
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base: cronekorkn:cf5b1396b1e789ffcdc8d247ef8fc25697003765
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cronekorkn:main
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compare: cronekorkn:3b483805df61031209014a01fae2044716037458
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cronekorkn:main

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cf5b1396b1 ... 3b483805df

Author SHA1 Message Date
CroneKorkN
3b483805df
wip 2025-06-01 19:31:52 +02:00
CroneKorkN
c165442acc
wip 2025-06-01 19:30:48 +02:00
1 changed files with 16 additions and 11 deletions
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27
process
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@ -23,7 +23,9 @@ DETECTION_DISTANCE_SECONDS = 30 # seconds (minimum time between detections)
DETECTION_DISTANCE_BLOCKS = DETECTION_DISTANCE_SECONDS // BLOCK_SECONDS # number of blocks to skip after a detection DETECTION_DISTANCE_BLOCKS = DETECTION_DISTANCE_SECONDS // BLOCK_SECONDS # number of blocks to skip after a detection
BLOCK_OVERLAP_FACTOR = 0.9 # overlap between blocks (0.2 means 20% overlap) BLOCK_OVERLAP_FACTOR = 0.9 # overlap between blocks (0.2 means 20% overlap)
MIN_SIGNAL_QUALITY = 1000.0 # maximum noise level (relative DB) to consider a detection valid MIN_SIGNAL_QUALITY = 1000.0 # maximum noise level (relative DB) to consider a detection valid
PLOT_PADDING_SECONDS = 2 # seconds (padding before and after the event in the plot) PLOT_PADDING_START_SECONDS = 2 # seconds (padding before and after the event in the plot)
PLOT_PADDING_END_SECONDS = 3 # seconds (padding before and after the event in the plot)
def process_recording(filename): def process_recording(filename):
print('processing', filename) print('processing', filename)
@ -107,26 +109,29 @@ def process_recording(filename):
sample_num += samples_per_block - overlapping_samples sample_num += samples_per_block - overlapping_samples
# 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, sound, samplerate):
# write a spectrogram using the sound from start to end of the event event_start_sample = current_event['start_sample'] - samplerate * PLOT_PADDING_START_SECONDS
event_start_sample = current_event['start_sample'] - samplerate * PLOT_PADDING_SECONDS event_end_sample = current_event['end_sample'] + samplerate * PLOT_PADDING_END_SECONDS
event_end_sample = current_event['end_sample'] + samplerate * PLOT_PADDING_SECONDS
event_clip = sound[event_start_sample:event_end_sample] event_clip = sound[event_start_sample:event_end_sample]
plot_start_date = current_event['start_at'] - datetime.timedelta(seconds=PLOT_PADDING_SECONDS) 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')
# write flac
flac_path = os.path.join(DETECTIONS_DIR, f"{filename_prefix}.flac")
soundfile.write(flac_path, event_clip, samplerate, format='FLAC')
# write spectrogram
plt.figure(figsize=(8, 6)) plt.figure(figsize=(8, 6))
plt.specgram(event_clip, Fs=samplerate, NFFT=samplerate, noverlap=samplerate//2, cmap='inferno', vmin=-100, vmax=-10) plt.specgram(event_clip, Fs=samplerate, NFFT=samplerate, noverlap=samplerate//2, cmap='inferno', vmin=-100, vmax=-10)
plt.title(f"Bootshorn @{plot_start_date.strftime('%Y-%m-%d %H:%M:%S')}") plt.title(f"Bootshorn @{event.strftime('%Y-%m-%d %H:%M:%S')}")
plt.xlabel("Time (s)") plt.xlabel("Time (s)")
plt.ylabel("Frequency (Hz)") plt.ylabel("Frequency (Hz)")
plt.colorbar(label="Intensity (dB)") plt.colorbar(label="Intensity (dB)")
plt.ylim(50, 1000) plt.ylim(50, 1000)
spectrogram_path = os.path.join(DETECTIONS_DIR, f"{current_event['start_at'].strftime('%Y-%m-%d_%H-%M-%S.%f%z')}.png") spectrogram_path = os.path.join(DETECTIONS_DIR, f"{filename_prefix}.png")
plt.savefig(spectrogram_path) plt.savefig(spectrogram_path)
plt.close() plt.close()
# write flac
flac_path = os.path.join(DETECTIONS_DIR, f"{current_event['start_at'].strftime('%Y-%m-%d_%H-%M-%S.%f%z')}.flac")
soundfile.write(flac_path, event_clip, samplerate, format='FLAC')
def main(): def main():