Analysis of Music Genre Classification Algorithms

Thesis Overview

The project titled "Analysis of Music Genre Classification Algorithms" aims to investigate and analyze the effectiveness of various algorithms in classifying music genres. Music genre classification is a fundamental task in music information retrieval, with applications ranging from music recommendation systems to content organization. The research will focus on evaluating the performance of different machine learning algorithms, such as Support Vector Machines, Random Forest, and Neural Networks, in accurately categorizing music tracks into predefined genres. The research will begin with a thorough review of existing literature on music genre classification algorithms to establish a foundation for the study. This literature review will explore the evolution of music genre classification techniques, highlighting the strengths and limitations of various approaches. By examining previous studies, the project aims to identify gaps in the current research and propose novel methodologies for enhancing music genre classification accuracy. Following the literature review, the research methodology will be outlined, detailing the dataset used, feature extraction techniques, model training procedures, and evaluation metrics. The project will utilize a diverse collection of music tracks spanning different genres to ensure the robustness and generalizability of the classification algorithms. Feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs) and spectral features will be employed to represent the audio content effectively. The core of the project will involve implementing and evaluating a variety of machine learning algorithms for music genre classification. By comparing the performance of different models on the same dataset, the research aims to identify the most effective algorithms for accurately categorizing music genres. The project will also investigate the impact of hyperparameter tuning, feature selection, and model ensembling on classification performance. The findings of the research will be presented and discussed in detail in the results chapter, highlighting the strengths and weaknesses of each algorithm and providing insights into the factors that influence classification accuracy. The discussion will also address the challenges encountered during the study and propose recommendations for future research in the field of music genre classification. In conclusion, the project "Analysis of Music Genre Classification Algorithms" seeks to contribute to the advancement of music information retrieval by evaluating the performance of state-of-the-art machine learning algorithms in genre classification. By conducting a comprehensive analysis of different approaches and methodologies, the research aims to enhance the accuracy and efficiency of music genre classification systems, ultimately benefiting applications such as music recommendation services and content organization platforms.