Dissecting the Genre of Nigerian Music with Machine Learning Models

Music Information Retrieval (MIR) is the task of extracting high-level information, such as genre, artist, or instrumentation, from music. Genre classification is an important and rapidly evolving research area of MIR. To date, only a small amount of research has been done on the automatic genre classification of Nigerian songs. Accordingly, this study uses the k-nearest neighbors (k-NN), SVM, random forest (RF), and XGBoost classifiers [1] for music genre classification, due to the robust accuracy of its ensemble tree methods. These classifiers were applied to timbral and tempo characteristic features mined from 478 Nigerian songs from 5 music genres: apala (100), juju (120), fuji (99), Highlife (120), and waka (39). The objective was to assess the quality of music genre classification using the ORIN dataset, based only on the analysis of these features. SHapley Additive exPlanations (SHAP) [2] values with TreeExplainer (Tree SHAP) [3] were obtained to explain the model predictions and show feature importance in descending order. Usually, these orderings are different for the three options (weight, gain, and cover) used to measure feature importance by XGBoost classifier, but are consistent and accurate for Tree SHAP. Hence, the SHAP method avoids the inconsistency problem of current methods, therefore, increasing the power to detect true feature dependencies in a dataset and aiding the building of SHAP summary plots, which succinctly display the magnitude, prevalence, and direction of a feature’s effect [3]. The unique contribution of this study is threefold: (i) to build a new song dataset, ORIN, which will serve as an addition to the collection of publicly available MIR datasets; (ii) to build an automatic form of music genre classification for Nigerian songs that can support or replace the manual method; and (iii) to introduce the global mean Tree SHAP method to show feature importance and impact on the classification model’s output.