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LPS_extraction

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The script is to extract log-power-spectrum (LPS) for speech enhancement and bandwidth extension.

Requirements

The model is implemented in PyTorch and uses several additional libraries. Specifically, we used:

  • pytorch==1.0
  • python==3.6.8
  • numpy==1.15.4
  • scipy==1.2.0

Setup

To install this package, simply clone the git repo:

git clone https://github.com/nanahou/LPS_extraction.git;
cd LPS_extraction;

Contents

The repository is structured as follows.

  • ./data: some audio samples from dataset[1]
  • audioread.py: the function to read audios
  • extract_LPS.py: the main scripts to extract features
  • normhamming.py: the function to apply a normalized square root hamming periodic window
  • plot_spectrum.py: the function to plot the LPS features
  • sigproc.py: including the functions to frame signals, deframe signals from [2]

Usage

  • If extracting LPS features, you only need to replace the path in extract_LPS.py with your own data path and run:

    python extract_LPS.py

  • If plotting your features, you only need to call the function in plot_spectrum.py.

Reference

[1]. Valentini-Botinhao, C., Wang, X., Takaki, S. and Yamagishi, J., 2016. Speech Enhancement for a Noise-Robust Text-to-Speech Synthesis System Using Deep Recurrent Neural Networks. In Interspeech (pp. 352-356).
[2]. https://github.com/jameslyons/python_speech_features/blob/master/python_speech_features/sigproc.py