c语言谱减法实现wav音频降噪输出例子

好的，以下是一个简单的C语言谱减法实现wav音频降噪的例子：

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

#define PI 3.14159265358979323846

typedef struct {
    uint32_t ChunkID;
    uint32_t ChunkSize;
    uint32_t Format;
    uint32_t Subchunk1ID;
    uint32_t Subchunk1Size;
    uint16_t AudioFormat;
    uint16_t NumChannels;
    uint32_t SampleRate;
    uint32_t ByteRate;
    uint16_t BlockAlign;
    uint16_t BitsPerSample;
    uint32_t Subchunk2ID;
    uint32_t Subchunk2Size;
} WavHeader;

int16_t* read_wav_file(char* filename, WavHeader* header) {
    FILE* fp = fopen(filename, "rb");
    if (fp == NULL) {
        printf("Error: cannot open file %s\n", filename);
        return NULL;
    }
    fread(header, sizeof(WavHeader), 1, fp);
    if (header->ChunkID != 0x46464952 || header->Format != 0x45564157
        || header->Subchunk1ID != 0x20746D66 || header->AudioFormat != 1
        || header->BitsPerSample != 16 || header->Subchunk2ID != 0x61746164) {
        printf("Error: invalid file format\n");
        fclose(fp);
        return NULL;
    }
    uint32_t num_samples = header->Subchunk2Size / (header->NumChannels * (header->BitsPerSample / 8));
    int16_t* buffer = (int16_t*)malloc(num_samples * sizeof(int16_t));
    fread(buffer, sizeof(int16_t), num_samples, fp);
    fclose(fp);
    return buffer;
}

void write_wav_file(char* filename, WavHeader* header, int16_t* buffer) {
    FILE* fp = fopen(filename, "wb");
    if (fp == NULL) {
        printf("Error: cannot create file %s\n", filename);
        return;
    }
    fwrite(header, sizeof(WavHeader), 1, fp);
    fwrite(buffer, sizeof(int16_t), header->Subchunk2Size/sizeof(int16_t), fp);
    fclose(fp);
}

double blackman_window(int n, int N) {
    return 0.42 - 0.5 * cos(2 * PI * n / (N-1)) + 0.08 * cos(4 * PI * n / (N-1));
}

void spectral_subtraction(int16_t* buffer, uint32_t num_samples, uint32_t sample_rate,
        uint16_t window_size, uint16_t overlap, double reduction_factor) {
    uint16_t num_windows = (num_samples - window_size) / (window_size - overlap) + 1;
    double* power_spec = (double*)malloc(window_size/2 * sizeof(double));
    double* noise_spec = (double*)malloc(window_size/2 * sizeof(double));
    double* noise_estimate = (double*)calloc(window_size/2, sizeof(double));
    double* noise_reduction = (double*)calloc(window_size/2, sizeof(double));
    double temp;
    uint16_t i, j, k, idx;
    for (i = 0; i < num_windows; i++) {
        // apply blackman window to the current window
        for (j = 0; j < window_size; j++) {
            temp = (double)buffer[i*(window_size-overlap) + j] / 32768.0;
            temp *= blackman_window(j, window_size);
            power_spec[j] = temp * temp;
        }
        // calculate noise estimate
        for (j = 0; j < window_size/2; j++) {
            noise_spec[j] = 0;
            for (k = 0; k < num_windows; k++) {
                idx = k*(window_size-overlap) + j;
                temp = (double)buffer[idx] / 32768.0;
                temp *= blackman_window(idx-k*(window_size-overlap), window_size);
                noise_spec[j] += temp * temp;
            }
            noise_spec[j] /= num_windows;
            noise_estimate[j] = reduction_factor * noise_spec[j] + (1 - reduction_factor) * noise_estimate[j];
        }
        // subtract noise estimate
        for (j = 0; j < window_size/2; j++) {
            if (power_spec[j] > noise_estimate[j]) {
                power_spec[j] -= noise_estimate[j];
            } else {
                power_spec[j] = 0;
            }
            noise_reduction[j] = noise_estimate[j] / (noise_estimate[j] + power_spec[j]);
        }
        // apply inverse FFT to obtain denoised window
        // (here we skip the inverse FFT part for simplicity, and just use the original window)
        for (j = 0; j < window_size; j++) {
            temp = (double)buffer[i*(window_size-overlap) + j] / 32768.0;
            buffer[i*(window_size-overlap) + j] = (int16_t)(temp * noise_reduction[j] * 32768.0);
        }
    }
    free(power_spec);
    free(noise_spec);
    free(noise_estimate);
    free(noise_reduction);
}

int main() {
    WavHeader header;
    int16_t* buffer = read_wav_file("input.wav", &header);
    if (buffer == NULL) {
        return 1;
    }
    spectral_subtraction(buffer, header.Subchunk2Size/sizeof(int16_t), header.SampleRate, 512, 256, 0.5);
    write_wav_file("output.wav", &header, buffer);
    free(buffer);
    return 0;
}

这个例子实现了基于谱减法的简单音频降噪，输入为一个16位带符号整数的wav文件，输出也是一个wav文件。程序首先读入文件头信息和音频数据，然后对每个大小为512的窗口（前后重叠256）分别进行以下操作：

1. 对当前窗口应用黑曼窗口，将时域信号转换到频域
2. 用所有窗口的平均功率谱作为噪声功率谱的估计值
3. 根据给定的降噪因子，计算当前窗口的噪声功率谱估计值，用它和信号功率谱进行比较
4. 如果信号功率谱大于噪声功率谱估计值，则将信号功率谱减去噪声功率谱估计值，否则将信号功率谱置为0
5. 用之前估计的噪声功率谱估计值和减法结果计算噪声的红减系数
6. 将红减系数应用于当前窗口的频域信号，得到去噪后的窗口信号

注意，这个实现的FFT部分没有包含在代码中，需要自己实现或使用第三方库。而且，这个实现可能并不完美，因此需要根据具体情况进行优化。