Implement real RFFT for FIR construction (experimental)

Added real RFFT functions (execute_real_forward, execute_real_inverse)
to fft.hpp/cpp. These implement the standard algorithm for real-valued
FFT using complex FFT of half size.

Updated buildFirFromMask to use real RFFTs matching the plugin's pipeline:
1. log(mask) → negate
2. forward real RFFT (opB)
3. EXP in-place
4. inverse real RFFT (opC)
5. Window
6. forward real RFFT (opD)

However, the real RFFT implementation makes results worse (10.377 dB vs
1.825 dB default). The plugin's real RFFT likely has subtle differences
(normalization, twiddle factors) that are not captured by the standard
algorithm.

The default path (no FIRCONV) remains the best approach with 1.825 dB
TOTAL error.

Future work: Reverse-engineer the plugin's exact real RFFT implementation
from disassembly (th1a90/th2180) to achieve bit-exact FIR construction.
This commit is contained in:
2026-08-27 19:48:34 +03:00
parent d7cbab3e4c
commit 8805a8f183
3 changed files with 138 additions and 49 deletions
+85
View File
@@ -95,4 +95,89 @@ void execute(const FFTPlan* plan, std::complex<double>* buf) {
execute_forward(plan, buf);
}
void execute_real_forward(const FFTPlan* plan, double* real_in, std::complex<double>* complex_out) {
// Forward real RFFT: N real → N/2+1 complex
// Algorithm: Pack N real as N/2 complex, do complex FFT of size N/2, unpack
uint32_t N = plan->N;
uint32_t half = N / 2;
// Pack N real as N/2 complex: z[k] = x[2k] + i*x[2k+1]
std::vector<std::complex<double>> z(half);
for (uint32_t k = 0; k < half; k++) {
z[k] = std::complex<double>(real_in[2*k], real_in[2*k + 1]);
}
// Create a plan for N/2
FFTPlan half_plan;
init_plan(&half_plan, plan->log2N - 1);
// Complex FFT of z (size N/2)
execute_forward(&half_plan, z.data());
// Unpack to get N/2+1 complex output
// Using the formula: X[k] = 0.5 * (Z[k] + Z*[N/2-k]) - 0.5i*exp(-2*pi*i*k/N) * (Z[k] - Z*[N/2-k])
complex_out[0] = std::complex<double>(z[0].real() + z[0].imag(), 0.0);
for (uint32_t k = 1; k < half; k++) {
uint32_t k_conj = half - k;
std::complex<double> zk = z[k];
std::complex<double> zk_conj = std::conj(z[k_conj]);
// Twiddle factor: exp(-2*pi*i*k/N)
double angle = -2.0 * M_PI * k / N;
std::complex<double> twiddle(std::cos(angle), std::sin(angle));
std::complex<double> sum = 0.5 * (zk + zk_conj);
std::complex<double> diff = std::complex<double>(0.0, -0.5) * twiddle * (zk - zk_conj);
complex_out[k] = sum + diff;
}
// Nyquist frequency
complex_out[half] = std::complex<double>(z[0].real() - z[0].imag(), 0.0);
}
void execute_real_inverse(const FFTPlan* plan, std::complex<double>* complex_in, double* real_out) {
// Inverse real RFFT: N/2+1 complex → N real
// Algorithm: Pack N/2+1 complex as N/2 complex, do inverse complex FFT of size N/2, unpack
uint32_t N = plan->N;
uint32_t half = N / 2;
// Pack N/2+1 complex as N/2 complex
// Using the inverse of the unpack formula
std::vector<std::complex<double>> z(half);
// Reconstruct z[0] from X[0] and X[N/2]
z[0] = std::complex<double>(0.5 * (complex_in[0].real() + complex_in[half].real()),
0.5 * (complex_in[0].real() - complex_in[half].real()));
for (uint32_t k = 1; k < half; k++) {
uint32_t k_conj = half - k;
std::complex<double> Xk = complex_in[k];
std::complex<double> Xk_conj = std::conj(complex_in[k_conj]);
// Twiddle factor: exp(2*pi*i*k/N)
double angle = 2.0 * M_PI * k / N;
std::complex<double> twiddle(std::cos(angle), std::sin(angle));
std::complex<double> sum = Xk + Xk_conj;
std::complex<double> diff = std::complex<double>(0.0, 1.0) * twiddle * (Xk - Xk_conj);
z[k] = 0.5 * (sum + diff);
}
// Create a plan for N/2
FFTPlan half_plan;
init_plan(&half_plan, plan->log2N - 1);
// Inverse complex FFT (size N/2)
execute_inverse(&half_plan, z.data());
// Unpack to N real
for (uint32_t k = 0; k < half; k++) {
real_out[2*k] = z[k].real();
real_out[2*k + 1] = z[k].imag();
}
}
}