wip

process

@@ -20,6 +20,7 @@ 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)
 
 
 def process_recording(filename):
@@ -35,6 +36,10 @@ def process_recording(filename):
     samplerate = info.samplerate
     block_samples = int(BLOCK_SECONDS * samplerate)
 
+    # initialize to a very old date
+    last_detection_at = datetime.datetime.min.replace(tzinfo=recording_date.tzinfo)
+    is_detecting = False
+
     # iterate blocks
     for num, block in enumerate(soundfile.blocks(path, blocksize=block_samples, overlap=int(block_samples*0.8))):
         block_date = recording_date + datetime.timedelta(seconds=num * BLOCK_SECONDS)
@@ -58,12 +63,23 @@ def process_recording(filename):
         max_freq_detected = DETECT_FREQUENCY_FROM <= max_freq <= DETECT_FREQUENCY_TO
         amplitude_detected = max_amplitude > AMPLITUDE_THRESHOLD
         low_noise_detected = noise < 0.1
+        no_recent_detection = is_detecting or (block_date - last_detection_at).total_seconds() > DETECTION_DISTANCE
 
         # conclude detection
-        if max_freq_detected and amplitude_detected and low_noise_detected:
+        if (
+            max_freq_detected and
+            amplitude_detected and
+            low_noise_detected and
+            no_recent_detection
+        ):
+            if not is_detecting:
+                is_detecting = True
+                last_detection_at = block_date
+                print("🔊")
+
             print(f'{block_date}: {max_amplitude:.1f}rDB @ {max_freq:.1f}Hz ({noise:.3f}rDB noise)')
-
-
+        else:
+            is_detecting = False