cronekorkn/bootshorn_investigation
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CroneKorkN 2025-06-01 18:08:04 +02:00
parent 7a7059f5c7
commit fc15d794a6
Signed by: cronekorkn
SSH key fingerprint: SHA256:v0410ZKfuO1QHdgKBsdQNF64xmTxOF8osF1LIqwTcVw
1 changed files with 13 additions and 9 deletions
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22
process
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@ -41,7 +41,10 @@ def process_recording(filename):
current_event = None
# read blocks of audio data with overlap from sound variable
for block_num, block in enumerate(soundfile.blocks(path, blocksize=samples_per_block, overlap=overlapping_samples)):
block_num = 0
for block in soundfile.blocks(path, blocksize=samples_per_block, overlap=overlapping_samples):
block_num += 1
# get block date and calculate FFT
block_date = recording_date + datetime.timedelta(seconds=block_num * (samples_per_block - overlapping_samples) / samplerate)
labels = rfftfreq(len(block), d=1/samplerate)
@ -49,15 +52,16 @@ def process_recording(filename):
amplitudes = np.abs(complex_amplitudes)
# get amplitudes only between 100 and 1000 Hz
adjacent_amplitudes = amplitudes[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
adjacent_labels = labels[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
search_amplitudes = amplitudes[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
search_labels = labels[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
# get the frequency with the highest amplitude
max_amplitude = max(adjacent_amplitudes)
max_amplitude_index = np.argmax(adjacent_amplitudes)
max_freq = adjacent_labels[max_amplitude_index]
max_amplitude = max(search_amplitudes)
max_amplitude_index = np.argmax(search_amplitudes)
max_freq = search_labels[max_amplitude_index]
# get the average amplitude of the adjacent frequencies
# get the average amplitude of the search frequencies
adjacent_amplitudes = search_amplitudes[(search_labels < DETECT_FREQUENCY_FROM) | (search_labels > DETECT_FREQUENCY_TO)]
noise = np.mean(adjacent_amplitudes)/max_amplitude
# check for detection criteria
@ -97,7 +101,7 @@ def process_recording(filename):
write_plot()
current_event = None
block_num += DETECTION_DISTANCE // BLOCK_SECONDS
block_num += (DETECTION_DISTANCE // BLOCK_SECONDS) * samples_per_block
block_num += 1
@ -114,7 +118,7 @@ def main():
os.makedirs(RECORDINGS_DIR, exist_ok=True)
os.makedirs(PROCESSED_RECORDINGS_DIR, exist_ok=True)
for filename in os.listdir(RECORDINGS_DIR):
for filename in sorted(os.listdir(RECORDINGS_DIR)):
if filename.endswith(".flac"):
process_recording(filename)