From d502b91cc8e627476fdf6d055245c469898f9c2f Mon Sep 17 00:00:00 2001
From: CroneKorkN <i@ckn.li>
Date: Sun, 1 Jun 2025 18:33:56 +0200
Subject: [PATCH] wip

---
 process | 27 ++++++++++++---------------
 1 file changed, 12 insertions(+), 15 deletions(-)

diff --git a/process b/process
index 0eb503c..12c353a 100755
--- a/process
+++ b/process
@@ -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():