Formula One® Track Classification by Machine Learning Applied on Car Engine Sound

Audio signal classification has been tackled with many machine learning (ML) tools for a diversity of applications. This paper presents a study on Formula One track classification based on the sound of the engine as registered by onboard microphones, including the collection, preprocessing, and curation of the audio data. We carry out a comparative of ML tools applied to this task. Additionally, we evaluate multiple feature extraction methods, including spectrograms, MFCCs, Fourier Transform, Laplace Transform, and Discrete Wavelet Transform assessing their impact on classification performance. The study compares the accuracy of traditional ML models, Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Experimental results demonstrate the effectiveness of the approach in Formula One track classification classification highlighting the strengths of various models and feature representations.