1. Objective: Use AI to generate music in traditional genres and create an interface for users to generate and listen to the music.
2. Focus: Preserve and innovate within traditional music forms, making them accessible to new audiences.
3. Tools: Magenta.js, TensorFlow, music datasets.
1. Install Python: Ensure Python is installed on your system.
2. Set Up Virtual Environment: Create a virtual environment to manage dependencies.
3. Install Required Libraries: Install OpenCV, TensorFlow, and other necessary libraries.
1. Find Datasets: Use music datasets from sources like the MusicNet dataset or other collections of traditional music.
2. Download Datasets: Ensure the datasets are in a suitable format (e.g., MIDI files).
Magenta Models: Use pre-trained models from the Magenta project (e.g., MusicVAE, PerformanceRNN).
MIDI Data Processing: Convert MIDI files into a format suitable for the model if necessary.
1. Generate Music Samples: Use the pre-trained model to generate music samples.
2. Save Generated Music: Save the generated music to a MIDI file.
Develop a Simple Interface: Use Magenta.js to create a web interface for generating and listening to AI-composed music.
1. Test with Different Inputs: Generate and listen to music samples using different input sequences.
2. Collect Feedback: Gather feedback from users to improve the interface and the quality of generated music.
1. Host the Interface: Deploy the web interface on a hosting platform (e.g., GitHub Pages, Heroku).
2. Monitor and Update: Continuously monitor the interface's performance and update the model and interface as needed.
1. Host the App: Deploy the app on a cloud platform (e.g., AWS, Heroku).
2. Monitor and Update: Continuously monitor the app's performance and update the model and information database as needed.
