Utilizing Machine Learning Algorithms for Music Genre Classification
Table Of Contents
Chapter ONE
INTRODUCTION
- 1.1Introduction
- 1.2Background of Study
- 1.3Problem Statement
- 1.4Objectives of Study
- 1.5Limitations of Study
- 1.6Scope of Study
- 1.7Significance of Study
- 1.8Structure of the Thesis
- 1.9Definition of Terms
Chapter TWO
LITERATURE REVIEW
- 2.1Overview of Music Genre Classification Studies
- 2.2Machine Learning Algorithms in Music Classification
- 2.3Previous Research on Music Genre Classification
- 2.4Impact of Music Genre Classification in the Music Industry
- 2.5Challenges in Music Genre Classification
- 2.6Trends in Music Genre Classification Research
- 2.7Evaluation Metrics in Music Genre Classification
- 2.8Data Collection for Music Genre Classification
- 2.9Feature Extraction in Music Genre Classification
- 2.10Music Genre Classification Applications
Chapter THREE
RESEARCH METHODOLOGY
- 3.1Research Design
- 3.2Data Collection Methods
- 3.3Sampling Techniques
- 3.4Data Analysis Tools
- 3.5Machine Learning Algorithms Selection
- 3.6Model Evaluation Techniques
- 3.7Validation Procedures
- 3.8Ethical Considerations in Research
Chapter FOUR
DATA PRESENTATION AND ANALYSIS
- Discussion of Findings
- 4.1Analysis of Experimental Results
- 4.2Comparison of Machine Learning Algorithms
- 4.3Interpretation of Classification Performance
- 4.4Discussion on Feature Importance
- 4.5Addressing Limitations and Challenges
- 4.6Implications for Music Genre Classification
- 4.7Recommendations for Future Research
Chapter FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
- and Summary
- 5.1Summary of Findings
- 5.2Conclusion
- 5.3Contributions to the Field
- 5.4Practical Implications
- 5.5Suggestions for Further Research
Thesis Abstract
AbstractThis thesis focuses on the application of machine learning algorithms for music genre classification. The project aims to explore the effectiveness of various machine learning techniques in automatically categorizing music into different genres. The study involves collecting and preprocessing a large dataset of music tracks, extracting relevant features from the audio signals, and training machine learning models to classify the music into predefined genres. The research methodology includes data collection, data preprocessing, feature extraction, model training, and evaluation. The study utilizes a diverse set of machine learning algorithms such as Support Vector Machines, Random Forest, and Convolutional Neural Networks to compare their performance in music genre classification. The findings of the study are discussed in detail, highlighting the strengths and limitations of each algorithm. The results show that machine learning algorithms can achieve high accuracy in classifying music genres, with certain algorithms outperforming others in specific scenarios. The significance of this research lies in its potential to enhance music recommendation systems, personalized playlists, and music discovery platforms. The conclusion summarizes the key findings of the study and provides recommendations for future research in the field of music genre classification using machine learning algorithms.
Thesis Overview
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