基于RNN的音频降噪算法 (附完整C代码)

前几天无意间看到一个项目rnnoise。

项目地址: https://github.com/xiph/rnnoise

基于RNN的音频降噪算法。

采用的是 GRU/LSTM 模型。

阅读下训练代码，可惜的是作者没有提供数据训练集。

不过基本可以断定他采用的数据集里，肯定有urbansound8k。

urbansound8k 数据集地址:

https://serv.cusp.nyu.edu/projects/urbansounddataset/urbansound8k.html

也可以考虑采用用作者训练的模型来构建数据集的做法，不过即费事，也麻烦。

经过实测，降噪效果很不错，特别是在背景噪声比较严重的情况下。

不过作者仅仅提供 pcm 的代码示例，并且还只支持48K采样率，

( 明显是为了兼容其另一个 项目  opus)

在很多应用场景下，这很不方便。

尽管稍微有点麻烦，但是事在人为，花了点时间，稍作修改。

具体修改如下：

1.支持wav格式

采用dr_wav(https://github.com/mackron/dr_libs/blob/master/dr_wav.h)

2.支持全部采样率

采样率的处理问题，采用简单粗暴法，

详情请移步博主另一篇小文《简洁明了的插值音频重采样算法例子 (附完整C代码)》

3.增加CMake文件

4.增加测试用 示例音频sample.wav

取自(https://github.com/orctom/rnnoise-java)

贴上完整示例代码 ：

#include <stdio.h>
#include "rnnoise.h"
#include <stdlib.h>
#include <stdint.h>

#define DR_WAV_IMPLEMENTATION

#include "dr_wav.h"

void wavWrite_int16(char *filename, int16_t *buffer, int sampleRate, uint32_t totalSampleCount) {
    drwav_data_format format;
    format.container = drwav_container_riff;
    format.format = DR_WAVE_FORMAT_PCM;
    format.channels = ;
    format.sampleRate = (drwav_uint32) sampleRate;
    format.bitsPerSample = ;
    drwav *pWav = drwav_open_file_write(filename, &format);
    if (pWav) {
        drwav_uint64 samplesWritten = drwav_write(pWav, totalSampleCount, buffer);
        drwav_uninit(pWav);
        if (samplesWritten != totalSampleCount) {
            fprintf(stderr, "ERROR\n");
            exit();
        }
    }
}

int16_t *wavRead_int16(char *filename, uint32_t *sampleRate, uint64_t *totalSampleCount) {
    unsigned int channels;
    int16_t *buffer = drwav_open_and_read_file_s16(filename, &channels, sampleRate, totalSampleCount);
    if (buffer == NULL) {
        fprintf(stderr, "ERROR\n");
        exit();
    }
    ) {
        drwav_free(buffer);
        buffer = NULL;
        *sampleRate = ;
        *totalSampleCount = ;
    }
    return buffer;
}

void splitpath(const char *path, char *drv, char *dir, char *name, char *ext) {
    const char *end;
    const char *p;
    const char *s;
    ] && path[] == ':') {
        if (drv) {
            *drv++ = *path++;
            *drv++ = *path++;
            *drv = '\0';
        }
    } else if (drv)
        *drv = '\0';
    for (end = path; *end && *end != ':';)
        end++;
    for (p = end; p > path && *--p != '\\' && *p != '/';)
        if (*p == '.') {
            end = p;
            break;
        }
    if (ext)
        for (s = end; (*ext = *s++);)
            ext++;
    for (p = end; p > path;)
        if (*--p == '\\' || *p == '/') {
            p++;
            break;
        }
    if (name) {
        for (s = p; s < end;)
            *name++ = *s++;
        *name = '\0';
    }
    if (dir) {
        for (s = path; s < p;)
            *dir++ = *s++;
        *dir = '\0';
    }
}

void resampleData(const int16_t *sourceData, int32_t sampleRate, uint32_t srcSize, int16_t *destinationData,
                  int32_t newSampleRate) {
    if (sampleRate == newSampleRate) {
        memcpy(destinationData, sourceData, srcSize * sizeof(int16_t));
        return;
    }
    uint32_t last_pos = srcSize - ;
    uint32_t dstSize = (uint32_t) (srcSize * ((float) newSampleRate / sampleRate));
    ; idx < dstSize; idx++) {
        float index = ((float) idx * sampleRate) / (newSampleRate);
        uint32_t p1 = (uint32_t) index;
        float coef = index - p1;
        uint32_t p2 = (p1 == last_pos) ? last_pos : p1 + ;
        destinationData[idx] = (int16_t) ((1.0f - coef) * sourceData[p1] + coef * sourceData[p2]);
    }
}

void f32_to_s16(int16_t *pOut, const float *pIn, size_t sampleCount) {
    if (pOut == NULL || pIn == NULL) {
        return;
    }
    ; i < sampleCount; ++i) {
        *pOut++ = (short) pIn[i];
    }
}

void s16_to_f32(float *pOut, const int16_t *pIn, size_t sampleCount) {
    if (pOut == NULL || pIn == NULL) {
        return;
    }
    ; i < sampleCount; ++i) {
        *pOut++ = pIn[i];
    }
}

void denoise_proc(int16_t *buffer, uint32_t buffen_len) {
    ;
    DenoiseState *st;
    st = rnnoise_create();
    float patch_buffer[frame_size];
    if (st != NULL) {
        uint32_t frames = buffen_len / frame_size;
        uint32_t lastFrame = buffen_len % frame_size;
        ; i < frames; ++i) {
            s16_to_f32(patch_buffer, buffer, frame_size);
            rnnoise_process_frame(st, patch_buffer, patch_buffer);
            f32_to_s16(buffer, patch_buffer, frame_size);
            buffer += frame_size;
        }
        ) {
            memset(patch_buffer, , frame_size * sizeof(float));
            s16_to_f32(patch_buffer, buffer, lastFrame);
            rnnoise_process_frame(st, patch_buffer, patch_buffer);
            f32_to_s16(buffer, patch_buffer, lastFrame);
        }
    }
    rnnoise_destroy(st);
}

void rnnDeNoise(char *in_file, char *out_file) {
    uint32_t in_sampleRate = ;
    uint64_t in_size = ;
    int16_t *data_in = wavRead_int16(in_file, &in_sampleRate, &in_size);
    uint32_t out_sampleRate = ;
    uint32_t out_size = (uint32_t) (in_size * ((float) out_sampleRate / in_sampleRate));
    int16_t *data_out = (int16_t *) malloc(out_size * sizeof(int16_t));
    if (data_in != NULL && data_out != NULL) {
        resampleData(data_in, in_sampleRate, (uint32_t) in_size, data_out, out_sampleRate);
        denoise_proc(data_out, out_size);
        resampleData(data_out, out_sampleRate, (uint32_t) out_size, data_in, in_sampleRate);
        wavWrite_int16(out_file, data_in, in_sampleRate, (uint32_t) in_size);
        free(data_in);
        free(data_out);
    } else {
        if (data_in) free(data_in);
        if (data_out) free(data_out);
    }
}

int main(int argc, char **argv) {
    printf("Audio Noise Reduction\n");
    printf("blog:http://tntmonks.cnblogs.com/\n");
    printf("e-mail:gaozhihan@vip.qq.com\n");
    )
        ;

    ];
    ];
    ];
    ];
    ];
    ];
    splitpath(in_file, drive, dir, fname, ext);
    sprintf(out_file, "%s%s%s_out%s", drive, dir, fname, ext);
    rnnDeNoise(in_file, out_file);
    printf("press any key to exit.\n");
    getchar();
    ;
}

不多写注释，直接看代码吧。

项目地址：https://github.com/cpuimage/rnnoise

示例具体流程为：

加载wav(拖放wav文件到可执行文件上)->重采样降噪->保存wav

若有其他相关问题或者需求也可以邮件联系俺探讨。

邮箱地址是: 
gaozhihan@vip.qq.com