Analysis of Music Genre Classification Techniques Using Machine Learning Algorithms

Thesis Overview

The project titled "Analysis of Music Genre Classification Techniques Using Machine Learning Algorithms" aims to explore and evaluate the effectiveness of applying machine learning algorithms in the classification of music genres. Music genre classification is a fundamental task in music information retrieval, with applications in recommendation systems, music streaming services, and music analysis. Traditional methods of music genre classification often rely on manual feature extraction and rule-based systems, which can be labor-intensive and may not capture the complex patterns present in music data. Machine learning algorithms offer a promising alternative by automatically learning patterns and relationships within the music data. This project seeks to investigate the performance of various machine learning techniques, such as Support Vector Machines, Random Forest, and Neural Networks, in accurately classifying music genres based on audio features. By leveraging a large dataset of music samples spanning multiple genres, the project aims to compare the effectiveness of different algorithms in accurately predicting genre labels. The research will also explore the impact of feature selection and dimensionality reduction techniques on the classification performance, aiming to identify the most relevant features for distinguishing between music genres. Additionally, the project will investigate the scalability and computational efficiency of the machine learning algorithms in handling large music datasets, considering real-world applications where processing speed and memory usage are critical factors. Furthermore, the project will conduct a comprehensive evaluation of the classification results, considering metrics such as accuracy, precision, recall, and F1 score to assess the performance of the algorithms across different music genres. The findings of this research will provide valuable insights into the strengths and limitations of using machine learning algorithms for music genre classification and offer practical recommendations for improving classification accuracy and efficiency. Overall, this project represents a significant contribution to the field of music information retrieval by advancing the understanding of how machine learning algorithms can be effectively applied to automate the classification of music genres. The outcomes of this research have the potential to enhance music recommendation systems, music indexing, and content-based music retrieval, ultimately benefiting both music enthusiasts and industry professionals in the music domain.