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CroneKorkN 2025-06-01 18:33:56 +02:00
parent e126ca829d
commit d502b91cc8
Signed by: cronekorkn
SSH key fingerprint: SHA256:v0410ZKfuO1QHdgKBsdQNF64xmTxOF8osF1LIqwTcVw
1 changed files with 12 additions and 15 deletions
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27
process
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@ -18,11 +18,10 @@ DETECT_FREQUENCY_TOLERANCE = 2 # Hz
DETECT_FREQUENCY_FROM = DETECT_FREQUENCY - DETECT_FREQUENCY_TOLERANCE # Hz
DETECT_FREQUENCY_TO = DETECT_FREQUENCY + DETECT_FREQUENCY_TOLERANCE # Hz
ADJACENCY_FACTOR = 2 # area to look for noise around the target frequency
AMPLITUDE_THRESHOLD = 200 # relative DB (rDB) (because not calibrated)
BLOCK_SECONDS = 3 # seconds (longer means more frequency resolution, but less time resolution)
DETECTION_DISTANCE = 30 # seconds (minimum time between detections)
BLOCK_OVERLAP_FACTOR = 0.8 # overlap between blocks (0.8 means 80% overlap)
MAX_NOISE = 0.1 # maximum noise level (relative DB) to consider a detection valid
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
def process_recording(filename):
print('processing', filename)
@ -41,12 +40,12 @@ def process_recording(filename):
current_event = None
# read blocks of audio data with overlap from sound variable
block_num = 0
sample_num = 0
for block in soundfile.blocks(path, blocksize=samples_per_block, overlap=overlapping_samples):
block_num += 1
sample_num += samples_per_block - overlapping_samples
# 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=sample_num / samplerate)
labels = rfftfreq(len(block), d=1/samplerate)
complex_amplitudes = rfft(block)
amplitudes = np.abs(complex_amplitudes)
@ -61,19 +60,17 @@ def process_recording(filename):
max_freq = search_labels[max_amplitude_index]
# 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
adjacent_amplitudes = amplitudes[(labels < DETECT_FREQUENCY_FROM) | (labels > DETECT_FREQUENCY_TO)]
signal_quality = max_amplitude/np.mean(adjacent_amplitudes)
# check for detection criteria
max_freq_detected = DETECT_FREQUENCY_FROM <= max_freq <= DETECT_FREQUENCY_TO
amplitude_detected = max_amplitude > AMPLITUDE_THRESHOLD
low_noise_detected = noise < MAX_NOISE
good_signal_quality = signal_quality > MIN_SIGNAL_QUALITY
# conclude detection
if (
max_freq_detected and
amplitude_detected and
low_noise_detected
good_signal_quality
):
# detecting an event
if not current_event:
@ -90,7 +87,7 @@ def process_recording(filename):
'end_freq': max_freq,
'max_amplitude': max(max_amplitude, current_event['max_amplitude']),
})
print(f'- {block_date.strftime('%Y-%m-%d %H:%M:%S')}: {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 (signal {signal_quality:.3f}x)')
else:
# not detecting an event
if current_event:
@ -101,9 +98,9 @@ def process_recording(filename):
write_plot()
current_event = None
block_num += (DETECTION_DISTANCE // BLOCK_SECONDS) * samples_per_block
#block_num += (DETECTION_DISTANCE // BLOCK_SECONDS) * samples_per_block
block_num += 1
#block_num += 1
def write_clip():