96 lines
No EOL
3.6 KiB
Python
Executable file
96 lines
No EOL
3.6 KiB
Python
Executable file
#!/usr/bin/env python3
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import os
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import concurrent.futures
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import datetime
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import numpy as np
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import matplotlib.pyplot as plt
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import soundfile
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import scipy.signal
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from scipy.fft import rfft, rfftfreq
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import shutil
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RECORDINGS_DIR = "recordings"
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PROCESSED_RECORDINGS_DIR = "recordings/processed"
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DETECTIONS_DIR = "events"
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DETECT_FREQUENCY = 211 # Hz
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DETECT_FREQUENCY_TOLERANCE = 2 # Hz
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DETECT_FREQUENCY_FROM = DETECT_FREQUENCY - DETECT_FREQUENCY_TOLERANCE # Hz
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DETECT_FREQUENCY_TO = DETECT_FREQUENCY + DETECT_FREQUENCY_TOLERANCE # Hz
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ADJACENCY_FACTOR = 2 # area to look for noise around the target frequency
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AMPLITUDE_THRESHOLD = 200 # relative DB (rDB) (because not calibrated)
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BLOCK_SECONDS = 3 # seconds (longer means more frequency resolution, but less time resolution)
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DETECTION_DISTANCE = 30 # seconds (minimum time between detections)
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def process_recording(filename):
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print('processing', filename)
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# get ISO 8601 nanosecond recording date from filename
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date_string_from_filename = os.path.splitext(filename)[0]
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recording_date = datetime.datetime.strptime(date_string_from_filename, "%Y-%m-%d_%H-%M-%S.%f%z")
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# get samplerate and blocksize
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path = os.path.join(RECORDINGS_DIR, filename)
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info = soundfile.info(path)
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samplerate = info.samplerate
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block_samples = int(BLOCK_SECONDS * samplerate)
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# initialize to a very old date
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last_detection_at = datetime.datetime.min.replace(tzinfo=recording_date.tzinfo)
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is_detecting = False
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# iterate blocks
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for num, block in enumerate(soundfile.blocks(path, blocksize=block_samples, overlap=int(block_samples*0.8))):
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block_date = recording_date + datetime.timedelta(seconds=num * BLOCK_SECONDS)
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complex_amplitudes = rfft(block)
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amplitudes = np.abs(complex_amplitudes)
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labels = rfftfreq(len(block), d=1/samplerate)
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# get amplitudes only between 100 and 1000 Hz
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adjacent_amplitudes = amplitudes[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
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adjacent_labels = labels[(labels >= DETECT_FREQUENCY_FROM/ADJACENCY_FACTOR) & (labels <= DETECT_FREQUENCY_TO*ADJACENCY_FACTOR)]
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# get the frequency with the highest amplitude
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max_amplitude = max(adjacent_amplitudes)
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max_amplitude_index = np.argmax(adjacent_amplitudes)
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max_freq = adjacent_labels[max_amplitude_index]
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# get the average amplitude of the adjacent frequencies
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noise = np.mean(adjacent_amplitudes)/max_amplitude
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# check for detection criteria
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max_freq_detected = DETECT_FREQUENCY_FROM <= max_freq <= DETECT_FREQUENCY_TO
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amplitude_detected = max_amplitude > AMPLITUDE_THRESHOLD
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low_noise_detected = noise < 0.1
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no_recent_detection = is_detecting or (block_date - last_detection_at).total_seconds() > DETECTION_DISTANCE
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# conclude detection
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if (
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max_freq_detected and
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amplitude_detected and
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low_noise_detected and
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no_recent_detection
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):
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if not is_detecting:
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is_detecting = True
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last_detection_at = block_date
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print("🔊")
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print(f'{block_date}: {max_amplitude:.1f}rDB @ {max_freq:.1f}Hz ({noise:.3f}rDB noise)')
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else:
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is_detecting = False
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def main():
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os.makedirs(RECORDINGS_DIR, exist_ok=True)
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os.makedirs(PROCESSED_RECORDINGS_DIR, exist_ok=True)
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for filename in os.listdir(RECORDINGS_DIR):
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if filename.endswith(".flac"):
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process_recording(filename)
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if __name__ == "__main__":
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main() |