vs2019C语言fopen_s通过文件名读WAV文件内的数据，统计时域信号的均值、均方值、方差，计算信号的自协方差矩阵

下面是一个简单的示例代码，可以通过文件名读取WAV文件内的数据，并计算信号的时域统计量和自协方差矩阵：

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

#define BUFFER_SIZE 1024

struct WaveHeader {
    char chunkId[4];
    uint32_t chunkSize;
    char format[4];
    char subchunk1Id[4];
    uint32_t subchunk1Size;
    uint16_t audioFormat;
    uint16_t numChannels;
    uint32_t sampleRate;
    uint32_t byteRate;
    uint16_t blockAlign;
    uint16_t bitsPerSample;
    char subchunk2Id[4];
    uint32_t subchunk2Size;
};

int main(int argc, char *argv[]) {
    if (argc < 2) {
        printf("Usage: %s <filename>\n", argv[0]);
        return 1;
    }

    FILE *fp = NULL;
    fopen_s(&fp, argv[1], "rb");
    if (fp == NULL) {
        printf("Error opening file %s\n", argv[1]);
        return 1;
    }

    // Read WAV header
    struct WaveHeader header;
    fread(&header, sizeof(header), 1, fp);

    if (strncmp(header.chunkId, "RIFF", 4) != 0 ||
        strncmp(header.format, "WAVE", 4) != 0 ||
        strncmp(header.subchunk1Id, "fmt ", 4) != 0 ||
        header.audioFormat != 1) {
        printf("Invalid WAV file format\n");
        fclose(fp);
        return 1;
    }

    // Read audio data
    uint32_t numSamples = header.subchunk2Size / (header.bitsPerSample / 8);
    float *data = (float *) malloc(numSamples * sizeof(float));
    int16_t buffer[BUFFER_SIZE];
    int readSize = 0;
    uint32_t i = 0;
    while ((readSize = fread(buffer, sizeof(int16_t), BUFFER_SIZE, fp)) > 0) {
        for (int j = 0; j < readSize; j++) {
            data[i++] = buffer[j] / 32768.0f;
        }
    }
    fclose(fp);

    // Calculate time domain statistics
    float mean = 0.0f, variance = 0.0f, rms = 0.0f;
    for (i = 0; i < numSamples; i++) {
        mean += data[i];
        rms += data[i] * data[i];
    }
    mean /= numSamples;
    rms = sqrtf(rms / numSamples);
    for (i = 0; i < numSamples; i++) {
        variance += (data[i] - mean) * (data[i] - mean);
    }
    variance /= numSamples;

    printf("Mean: %f\n", mean);
    printf("RMS: %f\n", rms);
    printf("Variance: %f\n", variance);

    // Calculate autocovariance matrix
    uint32_t maxLag = 100;
    float *autoCov = (float *) malloc((maxLag + 1) * (maxLag + 1) * sizeof(float));
    for (uint32_t m = 0; m <= maxLag; m++) {
        for (uint32_t n = 0; n <= maxLag; n++) {
            float sum = 0.0f;
            for (i = 0; i < numSamples - m; i++) {
                sum += (data[i] - mean) * (data[i + m] - mean);
            }
            autoCov[m * (maxLag + 1) + n] = sum / (numSamples - m);
            if (m != n) {
                autoCov[n * (maxLag + 1) + m] = autoCov[m * (maxLag + 1) + n];
            }
        }
    }

    // Print autocovariance matrix
    for (uint32_t m = 0; m <= maxLag; m++) {
        for (uint32_t n = 0; n <= maxLag; n++) {
            printf("%f ", autoCov[m * (maxLag + 1) + n]);
        }
        printf("\n");
    }

    free(data);
    free(autoCov);
    return 0;
}

该代码首先通过 `fopen_s` 函数打开指定的 WAV 文件，并读取 WAV 文件头部信息。然后，它将 WAV 文件中的音频数据读入到一个浮点数数组中，将每个采样值除以 32768.0，以将其归一化到 [-1, 1] 的范围内。接下来，它计算了时域统计量（均值、均方值和方差），并使用这些统计量计算了自协方差矩阵。最后，它打印了自协方差矩阵的值。需要注意的是，该代码的自协方差矩阵是一个对称矩阵，并且仅计算了最多 100 个滞后样本的值。如果需要更高的分辨率和/或更大的滞后样本数量，请相应地更改 `maxLag` 变量的值。