c语言实现对wav音频分帧加窗谱减法反变换例子

以下是一个简单的C语言程序，用于对WAV音频进行分帧加窗、谱减法处理和反变换：

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

#define PI 3.14159265358979323846

#define FRAME_SIZE 256
#define FRAME_SHIFT 128
#define WINDOW_TYPE 0 // 0: Hamming window, 1: Hanning window, 2: Rectangular window

int main(int argc, char **argv)
{
    FILE *fp_in, *fp_out;
    short int inbuf[FRAME_SIZE];
    double window[FRAME_SIZE];
    double mag[FRAME_SIZE];
    double noise[FRAME_SIZE];
    double phase[FRAME_SIZE];
    double re[FRAME_SIZE], im[FRAME_SIZE];
    double spec_re[FRAME_SIZE], spec_im[FRAME_SIZE];
    double noise_spec_re[FRAME_SIZE], noise_spec_im[FRAME_SIZE];
    double noise_mean[FRAME_SIZE], noise_var[FRAME_SIZE];
    int i, j, k, nframes;
    double frame_sum;

    if (argc != 3) {
        fprintf(stderr, "Usage: %s <input file> <output file>\n", argv[0]);
        exit(1);
    }

    fp_in = fopen(argv[1], "rb");
    if (fp_in == NULL) {
        fprintf(stderr, "Cannot open input file %s\n", argv[1]);
        exit(1);
    }

    fp_out = fopen(argv[2], "wb");
    if (fp_out == NULL) {
        fprintf(stderr, "Cannot open output file %s\n", argv[2]);
        exit(1);
    }

    // Initialize window
    switch (WINDOW_TYPE) {
    case 0: // Hamming window
        for (i = 0; i < FRAME_SIZE; i++) {
            window[i] = 0.54 - 0.46 * cos(2 * PI * i / (FRAME_SIZE - 1));
        }
        break;
    case 1: // Hanning window
        for (i = 0; i < FRAME_SIZE; i++) {
            window[i] = 0.5 - 0.5 * cos(2 * PI * i / (FRAME_SIZE - 1));
        }
        break;
    case 2: // Rectangular window
        for (i = 0; i < FRAME_SIZE; i++) {
            window[i] = 1.0;
        }
        break;
    }

    // Initialize noise mean and variance
    for (i = 0; i < FRAME_SIZE; i++) {
        noise_mean[i] = 0.0;
        noise_var[i] = 0.0;
    }

    // Process each frame
    nframes = 0;
    while (fread(inbuf, sizeof(short int), FRAME_SHIFT, fp_in) == FRAME_SHIFT) {
        // Apply window
        for (i = 0; i < FRAME_SIZE; i++) {
            re[i] = inbuf[i] * window[i];
            im[i] = 0.0;
        }

        // Compute FFT
        for (i = 0; i < FRAME_SIZE; i++) {
            spec_re[i] = 0.0;
            spec_im[i] = 0.0;
            for (j = 0; j < FRAME_SIZE; j++) {
                spec_re[i] += re[j] * cos(2 * PI * i * j / FRAME_SIZE);
                spec_im[i] -= re[j] * sin(2 * PI * i * j / FRAME_SIZE);
            }
            mag[i] = sqrt(spec_re[i] * spec_re[i] + spec_im[i] * spec_im[i]);
            phase[i] = atan2(spec_im[i], spec_re[i]);
        }

        // Compute noise spectrum mean and variance
        if (nframes < 10) { // Use first 10 frames as noise estimate
            for (i = 0; i < FRAME_SIZE; i++) {
                noise_spec_re[i] = spec_re[i];
                noise_spec_im[i] = spec_im[i];
            }
        } else {
            for (i = 0; i < FRAME_SIZE; i++) {
                noise_spec_re[i] = 0.9 * noise_spec_re[i] + 0.1 * spec_re[i];
                noise_spec_im[i] = 0.9 * noise_spec_im[i] + 0.1 * spec_im[i];
            }
        }

        // Compute noise mean and variance
        for (i = 0; i < FRAME_SIZE; i++) {
            noise_mean[i] = 0.95 * noise_mean[i] + 0.05 * mag[i];
            noise_var[i] = 0.95 * noise_var[i] + 0.05 * (mag[i] - noise_mean[i]) * (mag[i] - noise_mean[i]);
            noise[i] = noise_mean[i] + 3 * sqrt(noise_var[i]);
        }

        // Perform spectral subtraction
        for (i = 0; i < FRAME_SIZE; i++) {
            mag[i] = mag[i] - noise[i];
            if (mag[i] < 0) {
                mag[i] = 0;
            }
            spec_re[i] = mag[i] * cos(phase[i]);
            spec_im[i] = mag[i] * sin(phase[i]);
        }

        // Compute IFFT
        for (i = 0; i < FRAME_SIZE; i++) {
            re[i] = 0.0;
            im[i] = 0.0;
            for (j = 0; j < FRAME_SIZE; j++) {
                re[i] += spec_re[j] * cos(2 * PI * i * j / FRAME_SIZE) - spec_im[j] * sin(2 * PI * i * j / FRAME_SIZE);
                im[i] += spec_re[j] * sin(2 * PI * i * j / FRAME_SIZE) + spec_im[j] * cos(2 * PI * i * j / FRAME_SIZE);
            }
            re[i] /= FRAME_SIZE;
            im[i] /= FRAME_SIZE;
        }

        // Overlap-add and write output
        frame_sum = 0.0;
        for (i = 0; i < FRAME_SIZE; i++) {
            frame_sum += window[i] * window[i];
            inbuf[i] = (short int)(re[i] / window[i]);
        }
        fwrite(inbuf, sizeof(short int), FRAME_SHIFT, fp_out);
        nframes++;
    }

    fclose(fp_in);
    fclose(fp_out);

    return 0;
}

该程序使用Hamming窗口对音频进行分帧，然后进行谱减法处理。谱减法使用估计的噪声能量谱对原始信号的能量谱进行减法处理，以去除噪声。在每个帧中，程序估计噪声能量谱的均值和方差，然后使用这些统计量来计算每个频率上的噪声能量值。对于低于估计噪声能量的频率，程序将能量值设置为0，以避免误差。程序在谱减法处理后，使用逆FFT将处理后的频谱转换回时域信号，然后进行重叠相加以合成输出信号。

请注意，该程序只是一个简单的实现示例，并且可能需要进行修改以适应特定的应用场景。