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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
This commit is contained in:
Dean Lee 2024-07-26 04:36:44 +08:00 committed by GitHub
parent 3be2c138b9
commit 757cb2eb6c
2 changed files with 13 additions and 11 deletions
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2
selfdrive/test/test_onroad.py
View File

@ -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,

22
system/micd.py
View File

@ -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: