Data-Driven Techniques for Music Genre Recognition
Sergio Santiago Rentería, Jesús L. Llano García, Francisco J. Cantú-Ortiz · 2020
After the digital revolution, it is not strange to see data science taking interest in music.The sheer amount of available content opens a plethora of possibilities for studying music and its social impact from a data analytic perspective.This paper studies the relationship that exists between, song features and their corresponding genre, to provide data-mining tools for music recommendation and sub-genre identification.For the first task, we compared different classification models, including Random Forests, Fully-connected neural networks and Logistic Regression.For the latter, we carried out cluster analysis and dimensionality reduction for data visualisation.Overall, Random Forest models had better performance in genre classification than Fully-connected networks, but they suffered from overfitting.Moreover, the highest accuracy obtained was too low (64%) to be of use for genre recognition applications.Nevertheless, we think our results show the limitations of hand-crafted features and point towards more sophisticated deep learning techniques.