Toy Example for WPE
Weighted Prediction Error
Background noise and signal reverberation due to reflections in an enclosure are the two main impairments in acoustic signal processing and far-field speech recognition. This work addresses signal dereverberation techniques based on WPE for speech recognition and other far-field applications. WPE is a compelling algorithm to blindly dereverberate acoustic signals based on long-term linear prediction.
Different implementations of “Weighted Prediction Error” for speech dereverberation
Yoshioka, Takuya, and Tomohiro Nakatani. “Generalization of multi-channel linear prediction methods for blind MIMO impulse response shortening.” IEEE Transactions on Audio, Speech, and Language Processing 20.10 (2012): 2707-2720.
This code has been tested with Python 3.5 and 3.6.
Clone the repository. Then install it as follows if you want to make changes to the code:
https://github.com/fgnt/nara_wpe.git cd nara_wpe pip install --editable .
Alternatively, if you just want to run it, install it directly with Pip from Github:
pip install git+https://github.com/fgnt/nara_wpe.git
Check the example notebook for further details. If you download the example notebook, you can listen to the input audio examples and to the dereverberated output too.
You can find some documentation here: nara-wpe.readthedocs.io.
Since 2017-09-05 a TensorFlow implementation has been added to nara_wpe. It has been tested with a few test cases against the Numpy implementation.
The first version of the Numpy implementation was written in June 2017 while Lukas Drude and Kateřina Žmolíková resided in Nara, Japan. The aim was to have a publicly available implementation of Takuya Yoshioka’s 2012 paper.
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