Classification of Music Genres using Feature Selection and Hyperparameter Tuning

Abstract

The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.

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“…The results show that the classifiers' performances ranked as follows: XGB achieved 96.61% for the accuracy measure, and KNN, RF, DT and NBC achieved accuracies of 94.91%, 88.13%, 86.44%, and 67.79%, respectively. The study of Singhal et al [82] classified music genres with Logistic Regression (LR), KNN, SVM, XGB, and RF classifiers. They also explored the effect of HPO on the RF classifier only, where the results were enhanced about 13% for accuracy and reached 98.8%.…”