/* * Rational Sample Rate Conversion * Copyright (C) 2012, 2013 Thomas Tsou * * This library is free software; you can redistribute it and/or * modify it under the terms of the GNU Lesser General Public * License as published by the Free Software Foundation; either * version 2.1 of the License, or (at your option) any later version. * * This library is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public * License along with this library; if not, write to the Free Software * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA */ #include #include #include #include #include #include "Resampler.h" extern "C" { #include "convolve.h" } #ifndef M_PI #define M_PI 3.14159265358979323846264338327f #endif #define MAX_OUTPUT_LEN 4096 static float sinc(float x) { if (x == 0.0) return 0.9999999999; return sin(M_PI * x) / (M_PI * x); } bool Resampler::initFilters(float bw) { size_t proto_len = p * filt_len; float *proto, val, cutoff; float sum = 0.0f, scale = 0.0f; float midpt = (float) (proto_len - 1.0) / 2.0; /* * Allocate partition filters and the temporary prototype filter * according to numerator of the rational rate. Coefficients are * real only and must be 16-byte memory aligned for SSE usage. */ proto = new float[proto_len]; if (!proto) return false; partitions = (float **) malloc(sizeof(float *) * p); if (!partitions) { free(proto); return false; } for (size_t i = 0; i < p; i++) { partitions[i] = (float *) memalign(16, filt_len * 2 * sizeof(float)); } /* * Generate the prototype filter with a Blackman-harris window. * Scale coefficients with DC filter gain set to unity divided * by the number of filter partitions. */ float a0 = 0.35875; float a1 = 0.48829; float a2 = 0.14128; float a3 = 0.01168; if (p > q) cutoff = (float) p; else cutoff = (float) q; for (size_t i = 0; i < proto_len; i++) { proto[i] = sinc(((float) i - midpt) / cutoff * bw); proto[i] *= a0 - a1 * cos(2 * M_PI * i / (proto_len - 1)) + a2 * cos(4 * M_PI * i / (proto_len - 1)) - a3 * cos(6 * M_PI * i / (proto_len - 1)); sum += proto[i]; } scale = p / sum; /* Populate filter partitions from the prototype filter */ for (size_t i = 0; i < filt_len; i++) { for (size_t n = 0; n < p; n++) { partitions[n][2 * i + 0] = proto[i * p + n] * scale; partitions[n][2 * i + 1] = 0.0f; } } /* For convolution, we store the filter taps in reverse */ for (size_t n = 0; n < p; n++) { for (size_t i = 0; i < filt_len / 2; i++) { val = partitions[n][2 * i]; partitions[n][2 * i] = partitions[n][2 * (filt_len - 1 - i)]; partitions[n][2 * (filt_len - 1 - i)] = val; } } delete proto; return true; } void Resampler::releaseFilters() { if (partitions) { for (size_t i = 0; i < p; i++) free(partitions[i]); } free(partitions); partitions = NULL; } static bool check_vec_len(int in_len, int out_len, int p, int q) { if (in_len % q) { std::cerr << "Invalid input length " << in_len << " is not multiple of " << q << std::endl; return false; } if (out_len % p) { std::cerr << "Invalid output length " << out_len << " is not multiple of " << p << std::endl; return false; } if ((in_len / q) != (out_len / p)) { std::cerr << "Input/output block length mismatch" << std::endl; std::cerr << "P = " << p << ", Q = " << q << std::endl; std::cerr << "Input len: " << in_len << std::endl; std::cerr << "Output len: " << out_len << std::endl; return false; } if (out_len > MAX_OUTPUT_LEN) { std::cerr << "Block length of " << out_len << " exceeds max of " << MAX_OUTPUT_LEN << std::endl; return false; } return true; } void Resampler::computePath() { for (int i = 0; i < MAX_OUTPUT_LEN; i++) { in_index[i] = (q * i) / p; out_path[i] = (q * i) % p; } } int Resampler::rotate(float *in, size_t in_len, float *out, size_t out_len) { int n, path; int hist_len = filt_len - 1; if (!check_vec_len(in_len, out_len, p, q)) return -1; /* Insert history */ memcpy(&in[-2 * hist_len], history, hist_len * 2 * sizeof(float)); /* Generate output from precomputed input/output paths */ for (size_t i = 0; i < out_len; i++) { n = in_index[i]; path = out_path[i]; convolve_real(in, in_len, partitions[path], filt_len, &out[2 * i], out_len - i, n, 1, 1, 0); } /* Save history */ memcpy(history, &in[2 * (in_len - hist_len)], hist_len * 2 * sizeof(float)); return out_len; } bool Resampler::init(float bw) { size_t hist_len = filt_len - 1; /* Filterbank filter internals */ if (initFilters(bw) < 0) return false; /* History buffer */ history = new float[2 * hist_len]; memset(history, 0, 2 * hist_len * sizeof(float)); /* Precompute filterbank paths */ in_index = new size_t[MAX_OUTPUT_LEN]; out_path = new size_t[MAX_OUTPUT_LEN]; computePath(); return true; } size_t Resampler::len() { return filt_len; } Resampler::Resampler(size_t p, size_t q, size_t filt_len) : in_index(NULL), out_path(NULL), partitions(NULL), history(NULL) { this->p = p; this->q = q; this->filt_len = filt_len; } Resampler::~Resampler() { releaseFilters(); delete history; delete in_index; delete out_path; }