Building a Music Genre Classification Model

In this mini project, we will develop a music genre classification model using machine learning techniques. The first step involves gathering a diverse dataset of audio files labeled by genre, such as pop, rock, jazz, and classical. We will preprocess the audio data by extracting relevant features, such as Mel-frequency cepstral coefficients (MFCCs), which capture the timbral characteristics of the music. After splitting the dataset into training and testing subsets, we will choose an appropriate machine learning algorithm, such as Random Forest or a Convolutional Neural Network (CNN), for the classification task. The model will be trained on the extracted features, optimizing its performance through techniques like cross-validation and hyperparameter tuning. Finally, we will evaluate the model’s accuracy and performance metrics on the test set, visualizing the results using confusion matrices and precision-recall curves. This project will provide insights into the application of machine learning in audio analysis and genre classification, demonstrating how algorithms can learn to identify distinct musical characteristics.