跳到主要內容

使用RNN模型作語音訊號的除噪

使用RNN模型作語音訊號的除噪

 O​riginal Voice Clip:錄下的人聲語音訊號

 O​ffice Noise:錄下的辦公室背景噪音

 Mixing:前２項混合而成的訊號

 After RNN:透過RNN模型除噪後的語音訊號波形

 By Directional Mic in F15K:筆電上指向型麥克風的所錄下除噪後的波形

結論：透過RNN模型除噪的功能，具有近似指向型麥克風的除噪功能。也就是以軟體處理的技術來取代實體裝置。

 From lower right 2 pictures, voice clip mixed with background noise was restored well in comparison to audio waveform recording by directional mic and original voice clip waveform.


Ref:
https://people.xiph.org/~jm/demo/rnnoise/
https://hacks.mozilla.org/2017/09/rnnoise-deep-learning-noise-suppression/
https://github.com/xiph/rnnoise

mic denoise rnn
        audacity:
        import -> raw data -> Signed 16bit/Little endian/one channel, 48000
        rnn
        frank@frank-GL753VD:~/1T/back0529/mic
        import format: Signed 16 bit mono format
        frank@frank-GL753VD:~/1T/back0529/mic
        ~/1T/back0529/mic/RNNtest/F15K/tsai1.raw  ---->recorded by 2 mic (directional mic)
        ~/1T/back0529/mic/RNNtest/AS3EA/OUTtsai1.pcm  ---->after RNN
        ~/1T/back0529/mic/RNNtest/AS3EA/tsai1.pcm  ----> after Mixing





留言

張貼留言

這個網誌中的熱門文章

增強式學習

   迴力球遊戲－ATARI     賽車遊戲DQN－ATARI 賽車遊戲－TORCS Ref:     李宏毅老師 YOUTUBE DRL 1-3 On-policy VS Off-policy On-policy     The agent learned and the agent interacting with the environment is the same     阿光自已下棋學習 Off-policy     The agent learned and the agent interacting with the environment is different     佐助下棋，阿光在旁邊看 Add a baseline:     It is possible that R is always positive     So R subtract a expectation value Policy in " Policy Gradient" means output action, like left/right/fire gamma-discounted rewards: 時間愈遠的貢獻，降低其權重 Reward Function & Action is defined in prior to training MC v.s. TD MC 蒙弟卡羅: critic after episode end : larger variance(cuz conditions differ a lot in every episode), unbiased (judge until episode end, more fair) TD: Temporal-difference approach: critic during episode :smaller variance, biased maybe atari : a3c  ...
張貼留言
閱讀完整內容

Frameworks overview

Picture courtesy from Paul Huang
張貼留言
閱讀完整內容

tensorflow

TensorFlow Docker requirements Install Docker  on your local  host  machine. For GPU support on Linux,  install nvidia-docker . Docker is the easiest way to enable TensorFlow  GPU support  on Linux since only the  NVIDIA® GPU driver  is required on the  host  machine  (the  NVIDIA® CUDA® Toolkit  does not need to be installed). docker run [-it] [--rm] [-p hostPort:containerPort] tensorflow/tensorflow[:tag] [command] $ docker run -it --rm tensorflow/tensorflow    python -c "import tensorflow as tf; print(tf.__version__)" Note:   nvidia-docker  v1 uses the  nvidia-docker  alias, where v2 uses  docker --runtime=nvidia . CUDA 9.0 for TensorFlow < 1.13.0 nvidia-docker2 intsall prerequisites: NVIDIA driver  and Docker  . If you have a custom  /etc/docker/daemon.json , the  nvidia-docker2  package might override it....
張貼留言
閱讀完整內容