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48 lines
1.5 KiB
C++

/**
* 3_fir.cpp
* Written by Clyne Sullivan.
*
* The below code was written for applying FIR filters. While this is still essentially an overlap-
* save convolution, other optimizations have been made to allow for larger filters to be applied
* within the available execution time. Samples are also normalized so that they center around zero.
*/
adcsample_t *process_data(adcsample_t *samples, unsigned int size)
{
static adcsample_t buffer[SIZE];
// Define the filter:
constexpr unsigned int filter_size = 3;
static float filter[filter_size] = {
// Put filter values here (note: precision will be truncated for 'float' size).
0.3333, 0.3333, 0.3333
};
// Do an overlap-save convolution
static adcsample_t prev[filter_size];
for (int n = 0; n < size; n++) {
// Using a float variable for accumulation allows for better code optimization
float v = 0;
for (int k = 0; k < filter_size; k++) {
int i = n - (filter_size - 1) + k;
auto s = i >= 0 ? samples[i] : prev[filter_size - 1 + i];
// Sample values are 0 to 4095. Below, the original sample is normalized to a -1.0 to
// 1.0 range for calculation.
v += (s / 2048.f - 1) * filter[k];
}
// Return value to sample range of 0-4095.
buffer[n] = (v + 1) * 2048.f;
}
// Save samples for next convolution
for (int i = 0; i < filter_size; i++)
prev[i] = samples[size - filter_size + i];
return buffer;
}