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Update iir.c

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This commit is contained in:
Kuba
2025-08-08 22:12:10 +02:00
committed by GitHub
parent 7350cbcf1e
commit c614536b6d
1 changed files with 14 additions and 38 deletions
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48
filter/iir.c
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@@ -15,47 +15,23 @@ inline float apply_preemphasis(ResistorCapacitor *filter, float sample) {
return out;
}
void tilt_init(TiltCorrectionFilter* filter, float correction_strength) {
// This filter is a first-order IIR low-shelf filter.
// The difference equation is: y[n] = b0*x[n] + b1*x[n-1] - a1*y[n-1]
// We simplify it to y[n] = x[n] - a1*y[n-1] which acts as a leaky integrator.
void tilt_init(TiltCorrectionFilter* filter, float cutoff_freq, float sample_rate) {
// High-pass filter: H(z) = (1 - z^-1) / (1 - α*z^-1)
float dt = 1.0f / sample_rate;
float tau = 1.0f / (2.0f * M_PI * cutoff_freq);
float alpha = tau / (tau + dt);
// The "correction_strength" is our leaky factor. It is the pole of the filter.
// A value close to 1.0 places the pole very close to the unit circle,
// providing a large boost to low frequencies (and DC).
if (correction_strength >= 1.0f) {
correction_strength = 0.99999f; // Prevent instability
}
filter->b0 = 1.0f;
filter->b1 = 0.0f;
filter->a1 = -correction_strength; // The feedback coefficient
// Reset filter state
filter->alpha = alpha;
filter->x_prev = 0.0f;
filter->y_prev = 0.0f;
}
float tilt(TiltCorrectionFilter* filter, float input_sample) {
// Apply the difference equation: y[n] = b0*x[n] + b1*x[n-1] - a1*y[n-1]
float output_sample = filter->b0 * input_sample + filter->b1 * filter->x_prev - filter->a1 * filter->y_prev;
float tilt(TiltCorrectionFilter* filter, float input) {
// High-pass filter: y[n] = α*y[n-1] + (x[n] - x[n-1])
float output = filter->alpha * filter->y_prev + (input - filter->x_prev);
// Important: Prevent output from running away due to DC offset accumulation
// This is a simple guard. If the filter becomes unstable or the output
// grows too large, it gets reset. For square waves, the absolute value of the
// output should not significantly exceed the absolute value of the input.
if (fabsf(output_sample) > 2.0f * fabsf(input_sample) && fabsf(input_sample) > 0.001f) {
// This condition indicates the filter state might be diverging. Resetting it.
// You may need to adjust the '2.0f' factor based on your signal.
filter->y_prev = 0;
output_sample = input_sample;
}
filter->x_prev = input;
filter->y_prev = output;
// Update the state for the next iteration
filter->x_prev = input_sample;
filter->y_prev = output_sample;
return output_sample;
return output;
}