Skip to content

Latest commit

 

History

History
50 lines (31 loc) · 1.72 KB

README.md

File metadata and controls

50 lines (31 loc) · 1.72 KB

Deep Denoising Autoencoder (DDAE) for Speech Enhancement

Tensorflow implementation of Speech Enhancement Based on Deep Denoising Autoencoder

Getting Started

Clone This repository to your local machine and run create_dir.sh first.

Prerequisites

  • python 3.5
  • tensorflow-gpu 1.8.0
  • scikit-learn 0.19.1
  • scipy 1.1.0
  • h5py 2.7.1
  • librosa 0.5.1
  • numpy 1.14.3
  • tqdm 4.23.2

Getting Started

  1. Download free dataset from VoxForge for clean data. Here I would recommed download cmu_us_awb_arctic.tgz
  2. Unzip clean dataset to /DeepDenoisingAutoencoder/data/raw/clean/
  3. Download free dataset from ESC-50 for noise data.
  4. Move ESC-50-master/audio to /DeepDenoisingAutoencoder/data/raw/noise/
  5. Set parameters in python/main.py
  6. Run python/main.py

Result

Spectrogram on Test data

Deployment

You can read many comments inside all .py files.

Authors

Yu-Ding Lu - Linkedin

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

  • Bio-ASP lab - CITI - Academia Sinica