Enhancing Music Genre Classification through Multi-Algorithm Analysis and User-Friendly Visualization

TL;DR

This paper introduces a multi-algorithm approach combined with user-friendly visualization to improve music genre classification accuracy and interpretability.

Contribution

It proposes using five independent algorithms for comprehensive analysis and visual tools for better user understanding, advancing music genre recognition methods.

Findings

Enhanced classification accuracy with multi-algorithm approach

Improved user engagement through visualization tools

Effective identification of complex music genres

Abstract

The aim of this study is to teach an algorithm how to recognize different types of music. Users will submit songs for analysis. Since the algorithm hasn't heard these songs before, it needs to figure out what makes each song unique. It does this by breaking down the songs into different parts and studying things like rhythm, melody, and tone via supervised learning because the program learns from examples that are already labelled. One important thing to consider when classifying music is its genre, which can be quite complex. To ensure accuracy, we use five different algorithms, each working independently, to analyze the songs. This helps us get a more complete understanding of each song's characteristics. Therefore, our goal is to correctly identify the genre of each submitted song. Once the analysis is done, the results are presented using a graphing tool, making it easy for users to understand and provide feedback.