This project is made to analyze in real time the audio recorded from the microphone with a neural network for the speech and music detection task. It can be used for many other tasks by changing the network and the post-processing.

The network is built with this project and the project is used as a sub-module for the pre-processing of the audio.

The application is built with Angular, Electron and Bootstrap. The backend is coded in Python with Flask.

The audio is recorded with MediaRecorder and then sent as a base64 data to the Python backend that build the .webm and then process the audio.

The data is transferred to the backend with a POST request.

• SoX (http://sox.sourceforge.net) is used for the resampling of the audio.

• avconv (https://libav.org/avconv.html) is used for the conversion from .webm to .wav.

• All the requirements of the submodule https://github.com/qlemaire22/speech-music-detection

• Make sure that npm and Node.js are installed.

• Install the dependencies with npm install.

• Put the trained network model.hdf5 in /model.

• To run the electron app: npm run electron-build

• To run only the web app: ng serve

• To run only the backend: python backend.py

The audio is recorded as a .webm then converted as a .wav and then resampled. This pipeline is quite complicated and it could be interesting to record a .wav with the good sampling rate directly.

The data is transferred to the backend with a simple POST request executed at a fixed interval. It might not be the most optimized way to do so.