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

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