#include "polyphase_resampler.h" #include #include #include "alnumbers.h" #include "opthelpers.h" namespace { constexpr double Epsilon{1e-9}; using uint = unsigned int; /* This is the normalized cardinal sine (sinc) function. * * sinc(x) = { 1, x = 0 * { sin(pi x) / (pi x), otherwise. */ double Sinc(const double x) { if(unlikely(std::abs(x) < Epsilon)) return 1.0; return std::sin(al::numbers::pi*x) / (al::numbers::pi*x); } /* The zero-order modified Bessel function of the first kind, used for the * Kaiser window. * * I_0(x) = sum_{k=0}^inf (1 / k!)^2 (x / 2)^(2 k) * = sum_{k=0}^inf ((x / 2)^k / k!)^2 */ constexpr double BesselI_0(const double x) { // Start at k=1 since k=0 is trivial. const double x2{x/2.0}; double term{1.0}; double sum{1.0}; int k{1}; // Let the integration converge until the term of the sum is no longer // significant. double last_sum{}; do { const double y{x2 / k}; ++k; last_sum = sum; term *= y * y; sum += term; } while(sum != last_sum); return sum; } /* Calculate a Kaiser window from the given beta value and a normalized k * [-1, 1]. * * w(k) = { I_0(B sqrt(1 - k^2)) / I_0(B), -1 <= k <= 1 * { 0, elsewhere. * * Where k can be calculated as: * * k = i / l, where -l <= i <= l. * * or: * * k = 2 i / M - 1, where 0 <= i <= M. */ double Kaiser(const double b, const double k) { if(!(k >= -1.0 && k <= 1.0)) return 0.0; return BesselI_0(b * std::sqrt(1.0 - k*k)) / BesselI_0(b); } // Calculates the greatest common divisor of a and b. constexpr uint Gcd(uint x, uint y) { while(y > 0) { const uint z{y}; y = x % y; x = z; } return x; } /* Calculates the size (order) of the Kaiser window. Rejection is in dB and * the transition width is normalized frequency (0.5 is nyquist). * * M = { ceil((r - 7.95) / (2.285 2 pi f_t)), r > 21 * { ceil(5.79 / 2 pi f_t), r <= 21. * */ constexpr uint CalcKaiserOrder(const double rejection, const double transition) { const double w_t{2.0 * al::numbers::pi * transition}; if LIKELY(rejection > 21.0) return static_cast(std::ceil((rejection - 7.95) / (2.285 * w_t))); return static_cast(std::ceil(5.79 / w_t)); } // Calculates the beta value of the Kaiser window. Rejection is in dB. constexpr double CalcKaiserBeta(const double rejection) { if LIKELY(rejection > 50.0) return 0.1102 * (rejection - 8.7); if(rejection >= 21.0) return (0.5842 * std::pow(rejection - 21.0, 0.4)) + (0.07886 * (rejection - 21.0)); return 0.0; } /* Calculates a point on the Kaiser-windowed sinc filter for the given half- * width, beta, gain, and cutoff. The point is specified in non-normalized * samples, from 0 to M, where M = (2 l + 1). * * w(k) 2 p f_t sinc(2 f_t x) * * x -- centered sample index (i - l) * k -- normalized and centered window index (x / l) * w(k) -- window function (Kaiser) * p -- gain compensation factor when sampling * f_t -- normalized center frequency (or cutoff; 0.5 is nyquist) */ double SincFilter(const uint l, const double b, const double gain, const double cutoff, const uint i) { const double x{static_cast(i) - l}; return Kaiser(b, x / l) * 2.0 * gain * cutoff * Sinc(2.0 * cutoff * x); } } // namespace // Calculate the resampling metrics and build the Kaiser-windowed sinc filter // that's used to cut frequencies above the destination nyquist. void PPhaseResampler::init(const uint srcRate, const uint dstRate) { const uint gcd{Gcd(srcRate, dstRate)}; mP = dstRate / gcd; mQ = srcRate / gcd; /* The cutoff is adjusted by half the transition width, so the transition * ends before the nyquist (0.5). Both are scaled by the downsampling * factor. */ double cutoff, width; if(mP > mQ) { cutoff = 0.475 / mP; width = 0.05 / mP; } else { cutoff = 0.475 / mQ; width = 0.05 / mQ; } // A rejection of -180 dB is used for the stop band. Round up when // calculating the left offset to avoid increasing the transition width. const uint l{(CalcKaiserOrder(180.0, width)+1) / 2}; const double beta{CalcKaiserBeta(180.0)}; mM = l*2 + 1; mL = l; mF.resize(mM); for(uint i{0};i < mM;i++) mF[i] = SincFilter(l, beta, mP, cutoff, i); } // Perform the upsample-filter-downsample resampling operation using a // polyphase filter implementation. void PPhaseResampler::process(const uint inN, const double *in, const uint outN, double *out) { if UNLIKELY(outN == 0) return; // Handle in-place operation. std::vector workspace; double *work{out}; if UNLIKELY(work == in) { workspace.resize(outN); work = workspace.data(); } // Resample the input. const uint p{mP}, q{mQ}, m{mM}, l{mL}; const double *f{mF.data()}; for(uint i{0};i < outN;i++) { // Input starts at l to compensate for the filter delay. This will // drop any build-up from the first half of the filter. size_t j_f{(l + q*i) % p}; size_t j_s{(l + q*i) / p}; // Only take input when 0 <= j_s < inN. double r{0.0}; if LIKELY(j_f < m) { size_t filt_len{(m-j_f+p-1) / p}; if LIKELY(j_s+1 > inN) { size_t skip{std::min(j_s+1 - inN, filt_len)}; j_f += p*skip; j_s -= skip; filt_len -= skip; } if(size_t todo{std::min(j_s+1, filt_len)}) { do { r += f[j_f] * in[j_s]; j_f += p; --j_s; } while(--todo); } } work[i] = r; } // Clean up after in-place operation. if(work != out) std::copy_n(work, outN, out); }