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fft over a rolling sample window for greater bin precision

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Jono Targett 2024-05-26 15:34:57 +09:30
parent cf8a9dcfc1
commit 8aecf684f9
1 changed files with 27 additions and 20 deletions
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17
scripts/selcal-fft.py
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@ -95,13 +95,18 @@ delta_f = sample_rate / segment_size
# Determine the bin range for desired frequency range (100 Hz to 2000 Hz)
high_bin = int(max_freq / delta_f)
seg_off = int(sample_rate * 0.1)
act_segs = len(filtered_data) // seg_off
# Initialize a 2D array to store DFT results (magnitude spectrum)
# Only store the bins within the desired frequency range
dft_results = np.zeros((num_segments, high_bin))
dft_results = np.zeros((act_segs, high_bin))
for i in range(num_segments):
start = i * segment_size
end = start + segment_size
for i in range(act_segs):
end = (i+1) * seg_off
start = end - segment_size
try:
segment = filtered_data[start:end]
# Step 4: Apply the DFT
@ -122,7 +127,9 @@ for i in range(num_segments):
codes = get_largest_two_indices(scores, 3.0)
if codes:
print([frequencies[code] for code in codes])
print([frequencies[code] for code in sorted(codes)])
except:
pass
# Step 5: Plot the spectrogram