广义互相关时延估计算法c语言

根据提供的引用内容，我们可以了解到广义互相关时延估计算法的核心在于对传播时延的确估计，一般通过对麦克风间信号做互相关处理得到。以下是一个基于GCC-PHAT算法的广义互相关时延估计的C语言实现的例子：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define PI 3.14159265358979323846

double gcc_phat(double *x, double *y, int len, int delay)
{
    double *x_pad, *y_pad, *xy_pad, *x_conj_pad;
    double *x_fft, *y_fft, *xy_fft, *x_conj_fft;
    double *corr, *icorr;
    double max_corr = 0.0;
    int max_delay = 0;
    int i;

    // Padding
    x_pad = (double *)calloc(2 * len, sizeof(double));
    y_pad = (double *)calloc(2 * len, sizeof(double));
    xy_pad = (double *)calloc(2 * len, sizeof(double));
    x_conj_pad = (double *)calloc(2 * len, sizeof(double));
    for (i = 0; i < len; i++) {
        x_pad[i] = x[i];
        y_pad[i] = y[i];
    }

    // FFT
    x_fft = (double *)calloc(2 * len, sizeof(double));
    y_fft = (double *)calloc(2 * len, sizeof(double));
    xy_fft = (double *)calloc(2 * len, sizeof(double));
    x_conj_fft = (double *)calloc(2 * len, sizeof(double));
    fft(x_pad, x_fft, len);
    fft(y_pad, y_fft, len);
    conj_fft(x_fft, x_conj_fft, len);

    // Cross-correlation
    corr = (double *)calloc(2 * len, sizeof(double));
    icorr = (double *)calloc(2 * len, sizeof(double));
    for (i = 0; i < 2 * len; i++) {
        xy_fft[i] = x_fft[i] * y_fft[i];
        x_conj_pad[i] = x_conj_fft[i] * y_fft[i];
    }
    ifft(xy_fft, corr, len);
    ifft(x_conj_pad, icorr, len);

    // Normalization
    for (i = 0; i < len; i++) {
        corr[i] /= len;
        icorr[i] /= len;
    }

    // Find maximum correlation
    for (i = 0; i < len; i++) {
        double re = corr[i + delay];
        double im = icorr[i + delay];
        double mag = sqrt(re * re + im * im);
        if (mag > max_corr) {
            max_corr = mag;
            max_delay = i;
        }
    }

    free(x_pad);
    free(y_pad);
    free(xy_pad);
    free(x_conj_pad);
    free(x_fft);
    free(y_fft);
    free(xy_fft);
    free(x_conj_fft);
    free(corr);
    free(icorr);

    return (double)max_delay;
}

void fft(double *x, double *y, int n)
{
    int i, j, k;
    double c, s, t1, t2;

    for (i = 0; i < n; i++) {
        y[2 * i] = x[i];
        y[2 * i + 1] = 0.0;
    }

    for (i = 0; i < n; i++) {
        j = i;
        k = n - 2;
        while (k >= 0) {
            k -= 2;
            if (j > k) {
                t1 = y[2 * j];
                t2 = y[2 * j + 1];
                y[2 * j] = y[2 * k];
                y[2 * j + 1] = y[2 * k + 1];
                y[2 * k] = t1;
                y[2 * k + 1] = t2;
            }
            k /= 2;
            j /= 2;
        }
    }

    for (i = 2; i <= n; i *= 2) {
        c = cos(-2 * PI / i);
        s = sin(-2 * PI / i);
        for (j = 0; j < n; j += i) {
            t1 = 1.0;
            t2 = 0.0;
            for (k = j; k < j + i / 2; k++) {
                double u1 = y[2 * k];
                double u2 = y[2 * k + 1];
                double v1 = y[2 * k + i];
                double v2 = y[2 * k + i + 1];
                y[2 * k] = t1 * v1 - t2 * v2;
                y[2 * k + 1] = t1 * v2 + t2 * v1;
                y[2 * k + i] = u1 - y[2 * k];
                y[2 * k + i + 1] = u2 - y[2 * k + 1];
                t1 = t1 * c - t2 * s;
                t2 = t1 * s + t2 * c;
            }
        }
    }
}

void conj_fft(double *x, double *y, int n)
{
    int i;

    for (i = 0; i < n; i++) {
        y[2 * i] = x[2 * i];
        y[2 * i + 1] = -x[2 * i + 1];
    }
}

void ifft(double *x, double *y, int n)
{
    int i;

    fft(x, y, n);

    for (i = 0; i < n; i++) {
        y[2 * i] /= n;
        y[2 * i + 1] /= n;
    }
}

int main()
{
    double x[] = {1.0, 2.0, 3.0, 4.0, 5.0};
    double y[] = {0.0, 0.0, 1.0, 2.0, 3.0};
    int len = 5;
    int delay = 2;
    double tau;

    tau = gcc_phat(x, y, len, delay);

    printf("Delay estimate: %f\n", tau);

    return 0;
}