关于效果问题，这里我解释下：
关于效果不好，我这里主要的原因是训练数据多样性不足，导致模型泛化比较差， 所以如果我提供的数据集（后文有下载链接）效果是可以的，那么我建议扩充数据集（或者数据下载链接整理后发我邮箱，里头应该还有些小细节以及和基于模型的算法配合需要尝试），至少涵盖你测试case的数据情况，应该也是可以的，但要普适性基于建模的方法应该是要的。
如果想着拿去直接商业化可能要失望，如果您的数据集效果不好（不论是算法生成的或是真实场景的），都可以给笔者传份，下载方式发送邮箱[email protected]，汇总后数据量够多的话，我会更新个更准确的模型，数据不足的话就算了（贡献数据的各位朋友也会按需在readme里一一感谢），另外如果对这个模型或者算法有兴趣的，比如增加信号处理的LP支持等，也可以申请这个库的开发者权限（也会按需readme里列出），欢迎各位朋友一起提升性能，谢谢~！
为了方便重现，这里把生成的训练数据和结果传了上来，应该比较大，只截取的数据含结果12G（这12G包含的训练集生成算法一样的，也许生成两三个G的结果训练集效果和这差不多），如果你只用我附带的部分数据training，我不清楚训练的结果是否能和我一样。 链接: https://pan.baidu.com/s/1mchtEFtFzKurR2Weum8IDA 提取码: kqm2 复制这段内容后打开百度网盘手机App，操作更方便哦
上传的数据是使用如下命令测试的，其中rnn_near.pcm是近端语音数据，rnn_far.pcm是远端语音数据，aec_out.pcm是aec结果数据，所有的数据均是16kHz，mono，raw 数据；
~/RNNAec/examples/rnnaec_demo dataset/rnn_near.pcm dataset/rnn_far.pcm aec_out.pcm
result.png是前几秒的效果图。 I also generated result.png for view.
RNNAec is a echo suppression library based on a recurrent neural network. I refered from RNNnoise Open Source Project.
To compile, just type: % ./autogen.sh % ./configure % make
Optionally: % make install
While it is meant to be used as a library, a simple command-line tool is provided as an example. It operates on RAW 16-bit (machine endian) mono PCM files sampled at 16 kHz. It can be used as:
./examples/rnnaec_demo <denoised && aeced output>
The output is also a 16-bit raw PCM file.
To Training for your own model, see next steps: %cd src && ./compile && cd - %./src/deecho_training dataset/rnn_far.pcm dataset/rnn_near.pcm dataset/aec_noise_far.pcm dataset/aec_noise_near.pcm 10000000 out.f32
%python ./training/bin2hdf5.py out.f32 XXXXX 138 training.h5
%python ./training/rnn_train.py training.h5
%python training/dump_rnn.py final_weights.hdf5 ./src/rnn_data.c rnn_data.h orig
%make clean & make %./examples/rnnaec_demo out.pcm
Next to research:
- Use this RNNAec as a NLP(Non-Linear Processing) module, LP(Linear Processing) module can use (speex or webrtc).
- NLP-RNNAec used to improve double-talk performance. Model based method LP(signal process) are more general for various devices and environment.
- Training dataset can add real captured, instead of just use clean speech and noise data(As above training example)
- This RNNAec can have denoise effect, Network layer may change as needed.
- Band may change as need.(I think should change)
- Pitch filter(I think use full band can replace this module, but computational complexity also rise)
Note: To get beset result, pls use clean raw data to train and test; use no delay for test Note: This repo just for demo, I just test a limited cases, If you have problems, pls describe detail training process && attach testing datas(pcm, wav)
If you like this repo, Please click star!!!
UPdate WebRTC AEC3 VS2019 soultion test module as a standalone module. May have time integrate as a linear processing module as Next to research part noted.