mcid.py: optimize FFT and A-weighting calculations (#33057)

* Precomputing weighting

* add comments back

* use cache

* spacing

spacing

* clean up

* lower by diff

---------

Co-authored-by: Shane Smiskol <shane@smiskol.com>
old-commit-hash: 313a2826c2

selfdrive/test/test_onroad.py

@@ -56,7 +56,7 @@ PROCS = {
   "system.logmessaged": 0.2,
   "system.tombstoned": 0,
   "logcatd": 0,
-  "system.micd": 6.0,
+  "system.micd": 5.0,
   "system.timed": 0,
   "selfdrive.pandad.pandad": 0,
   "system.statsd": 0.4,

system/micd.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 import numpy as np
+from functools import cache
 
 from cereal import messaging
 from openpilot.common.realtime import Ratekeeper
@@ -10,7 +11,16 @@ RATE = 10
 FFT_SAMPLES = 4096
 REFERENCE_SPL = 2e-5  # newtons/m^2
 SAMPLE_RATE = 44100
-SAMPLE_BUFFER = 4096 # (approx 100ms)
+SAMPLE_BUFFER = 4096  # approx 100ms
+
+
+@cache
+def get_a_weighting_filter():
+  # Calculate the A-weighting filter
+  # https://en.wikipedia.org/wiki/A-weighting
+  freqs = np.fft.fftfreq(FFT_SAMPLES, d=1 / SAMPLE_RATE)
+  A = 12194 ** 2 * freqs ** 4 / ((freqs ** 2 + 20.6 ** 2) * (freqs ** 2 + 12194 ** 2) * np.sqrt((freqs ** 2 + 107.7 ** 2) * (freqs ** 2 + 737.9 ** 2)))
+  return A / np.max(A)
 
 
 def calculate_spl(measurements):
@@ -27,16 +37,8 @@ def apply_a_weighting(measurements: np.ndarray) -> np.ndarray:
   # Generate a Hanning window of the same length as the audio measurements
   measurements_windowed = measurements * np.hanning(len(measurements))
 
-  # Calculate the frequency axis for the signal
-  freqs = np.fft.fftfreq(measurements_windowed.size, d=1 / SAMPLE_RATE)
-
-  # Calculate the A-weighting filter
-  # https://en.wikipedia.org/wiki/A-weighting
-  A = 12194 ** 2 * freqs ** 4 / ((freqs ** 2 + 20.6 ** 2) * (freqs ** 2 + 12194 ** 2) * np.sqrt((freqs ** 2 + 107.7 ** 2) * (freqs ** 2 + 737.9 ** 2)))
-  A /= np.max(A)  # Normalize the filter
-
   # Apply the A-weighting filter to the signal
-  return np.abs(np.fft.ifft(np.fft.fft(measurements_windowed) * A))
+  return np.abs(np.fft.ifft(np.fft.fft(measurements_windowed) * get_a_weighting_filter()))
 
 
 class Mic: