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。
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gaozhihan@vip.qq.com