Frequency Domain Variance-normalized Delayed Linear Prediction Algorithm

Introduction

This program is an implementation of variance-normalizied delayed linear prediction in time-frequency domain, which is aimed at speech dereverberation, known as weighted prediction error (WPE) method.

Requirements

MATLB Code
- signal processing toolbox
Python Code
- Python 3.x
- Numpy
- soundfile
- matplotlib (Optional)

Run the Demo

MATLAB code
- Just run the script file demo_fdndlp.m in MATLAB and the audio sample in wav_sample will be used.
- To use your own data, change the filepath and sample_name in demo_fdndlp.m.
- The configrations are gathered in config.m. Be careful to change the settings.

Python code

Usage:

bash
python wpe.py [-h] [-o OUTPUT] [-m MIC_NUM] [-n OUT_NUM] [-p ORDER] filename

To use the default configrations and the given audio sample, run:
```
bash
python wpe.py ../wav_sample/sample_4ch.wav
```

Layout

 ./
 +-- matlab/                          matlab code files
 |   +-- lib/
 |   |   +-- +util/                   utility functions
 |   |   |-- stftanalysis.m           
 |   |   |-- stftsynthesis.m
 |   |-- demo_fdndlp.m
 |   |-- fdndlp.m
 |   |-- config.m
 +-- python/                          python code files
 |   |-- wpe.py
 |   |-- stft.py
 +-- wav_sample/                      audio samples
 |   |-- sample_4ch.wav               reverberant speech
 |   |-- drv_sample_4ch.wav           dereverberated speech
 |-- README.md

Reference

WPE speech dereverberation

Nakatani T, Yoshioka T, Kinoshita K, et al. Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction[J]. IEEE Transactions on Audio Speech & Language Processing, 2010, 18(7):1717-1731.