Microphone-based rhythm game

Microphone Based Rythm Game
High Level Design
Hardware Design
Program Design
Results
Conclusion
Appendix
Introduction
For our project, we made a game inspired by Guitar Hero. However, rather than taking button press inputs that become
the sound on the other end based on if the right button is pressed, we decided to take the actually detected note
frequency as input and check that for correctness. This means the game is essentially testing your ability to play the
song in the right pitch and timing with a Guitar Hero display format. First, we tried to conceptually develop this
project in both how it would look and function, going off of our understanding of music and how Guitar Hero plays.
Then we sequentially designed the aspects of the game until we had a final result, starting with a serial input
version of Guitar Hero that used the 1-9 keys for button input and VGA display. Once that was done we implemented the
frequency check using an FFT to make the final version of our game, and add songs to that.

Detailed Project Description

Microphone-based rhythm game applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- RP2040
- C
- SPI
- VGA
- IR
- FFT

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (C, Arduino IDE) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface