cronekorkn/bootshorn_investigation
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CroneKorkN 2025-06-01 17:32:31 +02:00
parent 0ef1bdfb4b
commit 7a7059f5c7
Signed by: cronekorkn
SSH key fingerprint: SHA256:v0410ZKfuO1QHdgKBsdQNF64xmTxOF8osF1LIqwTcVw
1 changed files with 4 additions and 16 deletions
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20
process
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@ -33,32 +33,20 @@ def process_recording(filename):
# get data and metadata from recording # get data and metadata from recording
path = os.path.join(RECORDINGS_DIR, filename) path = os.path.join(RECORDINGS_DIR, filename)
sound, samplerate = soundfile.read(path) samplerate = soundfile.info(path).samplerate
samples_per_block = int(BLOCK_SECONDS * samplerate) samples_per_block = int(BLOCK_SECONDS * samplerate)
overlapping_samples = int(samples_per_block * BLOCK_OVERLAP_FACTOR) overlapping_samples = int(samples_per_block * BLOCK_OVERLAP_FACTOR)
# calculate a base amplitude for normalization
complex_amplitudes_global = rfft(sound)
base_amplitude = np.mean(np.abs(complex_amplitudes_global))
print(f'base amplitude: {base_amplitude:.5f}rDB')
# chache data about current event # chache data about current event
current_event = None current_event = None
# read blocks of audio data with overlap from sound variable # read blocks of audio data with overlap from sound variable
block_num = 0 for block_num, block in enumerate(soundfile.blocks(path, blocksize=samples_per_block, overlap=overlapping_samples)):
while block_num < (len(sound) // (samples_per_block - overlapping_samples)):
# get block of audio data
start_sample = block_num * (samples_per_block - overlapping_samples)
end_sample = start_sample + samples_per_block
block = sound[start_sample:end_sample]
# get block date and calculate FFT # get block date and calculate FFT
block_date = recording_date + datetime.timedelta(seconds=block_num * (samples_per_block - overlapping_samples) / samplerate) block_date = recording_date + datetime.timedelta(seconds=block_num * (samples_per_block - overlapping_samples) / samplerate)
labels = rfftfreq(len(block), d=1/samplerate) labels = rfftfreq(len(block), d=1/samplerate)
complex_amplitudes = rfft(block) complex_amplitudes = rfft(block)
absolute_amplitudes = np.abs(complex_amplitudes) amplitudes = np.abs(complex_amplitudes)
amplitudes = absolute_amplitudes / base_amplitude
# get amplitudes only between 100 and 1000 Hz # get amplitudes only between 100 and 1000 Hz
adjacent_amplitudes = amplitudes[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)] adjacent_amplitudes = amplitudes[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
@ -98,7 +86,7 @@ def process_recording(filename):
'end_freq': max_freq, 'end_freq': max_freq,
'max_amplitude': max(max_amplitude, current_event['max_amplitude']), 'max_amplitude': max(max_amplitude, current_event['max_amplitude']),
}) })
#print(f'{block_date}: {max_amplitude:.1f}rDB @ {max_freq:.1f}Hz (noise {noise:.3f}rDB)') print(f'- {block_date.strftime('%Y-%m-%d %H:%M:%S')}: {max_amplitude:.1f}rDB @ {max_freq:.1f}Hz (noise {noise:.3f}rDB)')
else: else:
# not detecting an event # not detecting an event
if current_event: if current_event: