Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis
Abstract
The growth of the entertainment industry around the world may be seen in the creation of new genres and the influx of artists and musicians into this field. Every day, a large amount of music is generated and released. The classification of these music based on genres and the recommendation of music to users is a crucial task for various music streaming platforms. Many artificial intelligence methods have been created to overcome this. Inadequate data for training is one of the biggest issues when it comes to building machine learning algorithm. A certain dataset contains a large number of redundant features, which may lead the models to overfit. Data filtering could be used to solve this issue. On the GTZAN data for music genre classification, this article constructed numerous Artificial Intelligence (AI) models and used a data filtering strategy. This study does a comparative analysis and discusses the results. The models developed and evaluated are Naive Bayes, Stochastic Gradient Descent, KNN, Decision trees, Random Forest, Support Vector Machine, Logistic Regression, Neural Nets, Cross Gradient Booster, Cross Gradient Booster (Random Forest) and XGBoost. Accuracy gained by Naive Bayes is 51.95%, Stochastic Gradient Descent 65.53%, KNN 80.58%, Decision trees  63.997%, Random Forest is 81.41% , Support Vector Machine 75.41%, Logistic Regression 69.77%, Neural Nets 67.73%, Cross Gradient Booster 90.22%, Cross Gradient Booster (Random Forest) 74.87%.Finally, XGBoost is the best performed machine learning with accuracy of 90.22%. This research outcomes will help to analyse music in different areas.
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DOI: http://dx.doi.org/10.26555/jiteki.v7i3.22327
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