Audio denoising (Speech-enchancement)

(Inference and weights) Author, blog and sourcecode

Vincent Belz : vincent.belz@gmail.com

Published in towards data science : Speech-enhancement with Deep learning

Repository: https://github.com/vbelz/Speech-enhancement

(Web application) Author: check license.

How to run

Web version

Clic here to see the demo of speech-enchancement in action for audio (<10min).

Docker version

To downloand the image and run the contaider in detach mode, run the code below.

docker container run -p 8501:8501 --rm -d pablogod/audio-denoising:latest

To shutdown the docker type this:

docker ps -aq # Check which id was assigned for the audio-denoising instance
docker stop <weird id of audio-denoising> # Type the id

Local computer

Run this code locally on Linux based distros:

# Clone and install requirements
git clone https://github.com/DZDL/audio-denoising
cd audio-denoising
pip3 install -r requirements.txt
# Run streamlit
streamlit run app.py
# Then a webapp will open, check console output.

Deploy docker on Heroku

Only maintainers of the repository can do this.

heroku login
docker ps
heroku container:login
heroku container:push web -a audio-denoising
heroku container:release web -a audio-denoising

References (Model code from Speech-enhancement)

Jansson, Andreas, Eric J. Humphrey, Nicola Montecchio, Rachel M. Bittner, Aparna Kumar and Tillman Weyde.Singing Voice Separation with Deep U-Net Convolutional Networks. ISMIR (2017).

[https://ejhumphrey.com/assets/pdf/jansson2017singing.pdf]

Grais, Emad M. and Plumbley, Mark D., Single Channel Audio Source Separation using Convolutional Denoising Autoencoders (2017).

[https://arxiv.org/abs/1703.08019]

Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham

[https://arxiv.org/abs/1505.04597]

K. J. Piczak. ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd Annual ACM Conference on Multimedia, Brisbane, Australia, 2015.

[DOI: http://dx.doi.org/10.1145/2733373.2806390]

License

• MIT license