Project Abstract

Abstract
This research project focuses on the analysis of music genre classification using machine learning algorithms. With the exponential growth of digital music content available today, the need for efficient and accurate music genre classification systems has become increasingly important. Machine learning algorithms offer promising solutions to this problem by enabling automated classification of music genres based on audio features extracted from music tracks. The research begins with an introduction that provides an overview of the significance of music genre classification, followed by a background study on existing methods and technologies used in this field. The problem statement highlights the challenges and limitations faced in current music genre classification systems, setting the stage for the research objectives that aim to develop a more effective and robust classification model. The scope of the study encompasses the analysis of various machine learning algorithms, including deep learning models, for their suitability in music genre classification tasks. The research methodology chapter outlines the data collection process, feature extraction techniques, model training, and evaluation methods used to assess the performance of the classification model. In the literature review chapter, ten key studies and works related to music genre classification and machine learning algorithms are discussed to provide a comprehensive understanding of the research domain. The discussion covers various approaches, methodologies, and outcomes of previous research, highlighting the advancements and challenges in this field. The findings chapter presents a detailed analysis of the experimental results obtained from training and testing the machine learning models on a dataset of music tracks. The discussion of findings chapter explores the performance metrics, including accuracy, precision, recall, and F1-score, to evaluate the effectiveness of the classification model in accurately predicting music genres. Finally, the conclusion chapter summarizes the key findings of the research, discusses the implications of the results, and provides recommendations for future research directions. The research contributes to the field of music genre classification by demonstrating the potential of machine learning algorithms in improving the accuracy and efficiency of genre classification systems. Overall, this research project provides valuable insights into the application of machine learning algorithms for music genre classification and serves as a foundation for further advancements in automated music genre classification systems.

Project Overview