Hi,
I have a block of code in my wildlife tracker that detects high/low beeps
in a frequency. When the signal is weak the smoothing is particularly
helpful at detecting real beeps from the background - but it causing me
issues with calculating beep length and SNR calculations - please let me
explain:
This is my smoothing:
# smoothing
samples = signal.convolve(samples, [1]*189, 'same')/189
189 is the number of expected samples and after smoothing looks like this (
triangular orange line / blue line before smoothing )
[image: 90b66410-2d68-4d5f-bc29-0eb568eb077a.png]
The high/low samples detection looks like:
# Get a boolean array for all samples higher or lower than the
threshold
self.threshold = np.median(samples) * 1.5 # go just above noise
floor
low_samples = samples < self.threshold
high_samples = samples >= self.threshold
# Compute the rising edge and falling edges by comparing the
current value to the next with
# the boolean operator & (if both are true the result is true) and
converting this to an index
# into the current array
rising_edge_idx = np.nonzero(low_samples[:-1] &
np.roll(high_samples, -1)[:-1])[0]
falling_edge_idx = np.nonzero(high_samples[:-1] &
np.roll(low_samples, -1)[:-1])[0]
if len(rising_edge_idx) > 0:
self.rising_edge = rising_edge_idx[0]
if len(falling_edge_idx) > 0:
self.falling_edge = falling_edge_idx[0]
While the smoothing (orange line) improves beep detection when the signal
is weak, it introduces two new issues:
1. Beep length calculations are off because of the extended length of "high
samples" and;
2. SNR calculations are considerably higher (and I am not sure which SNR is
"correct")
Any suggestions on how to work with this issue would be appreciated greatly.
Thanks
AL
