[ad_1]

**Introduction**

Music genre classification and recommendation have become increasingly important in the digital age, as the sheer volume of music available online makes it challenging for users to discover new music that aligns with their preferences. Deep learning, a subset of machine learning that mimics the human brain’s neural networks, has shown tremendous potential in various applications, including music genre classification.

This thesis aims to develop a deep learning-based system for music genre classification and recommendation. By leveraging the power of deep learning algorithms, we aim to improve the accuracy and efficiency of music genre classification, as well as provide users with personalized music recommendations based on their listening habits.

**Table of Contents**

**Chapter 1: Introduction**
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

**Chapter 2: Literature Review**
2.1 Overview of Music Genre Classification
2.2 Traditional Methods for Music Genre Classification
2.3 Deep Learning in Music Genre Classification
2.4 Music Recommendation Systems
2.5 Collaborative Filtering Techniques
2.6 Content-Based Recommendation Systems
2.7 Hybrid Recommendation Systems
2.8 Evaluation Metrics for Recommendation Systems
2.9 Challenges in Music Genre Classification and Recommendation
2.10 Gaps in Existing Literature

**Chapter 3: Research Methodology**
3.1 Data Collection
3.2 Data Preprocessing
3.3 Feature Extraction
3.4 Model Selection
3.5 Model Training
3.6 Evaluation Metrics
3.7 Cross-Validation Techniques
3.8 Hyperparameter Tuning

**Chapter 4: Discussion of Findings**
4.1 Performance Evaluation of the Deep Learning Model
4.2 Comparison with Traditional Methods
4.3 User Study on Music Recommendation
4.4 Challenges Faced During Implementation
4.5 Future Research Directions

**Chapter 5: Conclusion and Summary**
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Music Industry
5.4 Limitations of the Study
5.5 Recommendations for Future Research

**Thesis Overview**
Music genre classification and recommendation are crucial tasks in the digital era, where the vast amount of music available online makes it challenging for users to discover new music that aligns with their preferences. This thesis aims to develop a deep learning-based system for music genre classification and recommendation, leveraging the potential of deep learning algorithms to improve the accuracy and efficiency of music classification and provide personalized music recommendations to users.

The literature review will provide an overview of music genre classification techniques, traditional methods, deep learning approaches, and recommendation systems. The research methodology will detail the data collection, preprocessing, feature extraction, model selection, and evaluation metrics used in developing the deep learning model. The discussion of findings will present the performance evaluation of the model, comparison with traditional methods, user study results, challenges faced, and future research directions.

In conclusion, this thesis will contribute to the advancement of music genre classification and recommendation systems, with implications for the music industry and recommendations for future research.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.