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micd.py: optimize processing with real FFT and cached hanning window #33104

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micd.py: optimize processing with real FFT and cached hanning window #33104

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15 changes: 11 additions & 4 deletions system/micd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,11 +18,16 @@
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)
freqs = np.fft.rfftfreq(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)


@cache
def get_hanning_window(size):
return np.hanning(size)


def calculate_spl(measurements):
# https://www.engineeringtoolbox.com/sound-pressure-d_711.html
sound_pressure = np.sqrt(np.mean(measurements ** 2)) # RMS of amplitudes
Expand All @@ -35,10 +40,12 @@ def calculate_spl(measurements):

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))

hanning_windowed_measurements = measurements * get_hanning_window(len(measurements))
# Apply the A-weighting filter to the signal
return np.abs(np.fft.ifft(np.fft.fft(measurements_windowed) * get_a_weighting_filter()))
weighted_fft = np.fft.rfft(hanning_windowed_measurements) * get_a_weighting_filter()
weighted_measurements = np.fft.irfft(weighted_fft)

return np.abs(weighted_measurements)


class Mic:
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