Melody Creator

In this YouTube video, Kie Codes demonstrated a method for generating melodies using a genetic algorithm. The code for this example can be found on Github.

The current example requires the user to evaluate each generated melody, which can be a time-consuming task. To improve this process, we will use a separate evaluation network to predict a rating for each melody, allowing the evolutionary algorithm to run more efficiently.

Furthermore, we will implement an active learning approach to train the evaluation network. This means that when the network is uncertain about a melody’s rating, we will ask the user to provide a label and retrain the network with this new data.

By using swergio, we will be able to communicate between the independent components (Evolutionary Algorithm, Evaluation Network, and Active Learner) and streamline the process of collecting user feedback.

The full source code of the example can be found on GitHub.

Requirements

For this example we need to run Python code and the easiest way to set it up is to:

  1. Install Python > 3.8 (https://www.python.org/downloads/)

  2. If required install python3-venv sudo apt install python3-venv

  3. Create a virtual environment in the example folder python3 -m venv /venv

  4. Activate the virtual environment source \env\bin\activate

  5. Install required packages to virtual environment pip install -r requirements.txt

How to run it

To execute this example, which includes training the evaluation network and providing active labels for new data, we can run separate code blocks either through interactive python in VSCode from the 05_user_interface.py file or as a Jupyter notebook ``05_user_interface.ipynb`.

The script will start all necessary components and set up another swergio client to communicate the labeling of the new data as well as sending the starting command.

The final code block in the files will shut down all the components.

Details