Music genres are helpful means for recommending songs of preferences by containing characteristics related to instruments, musical rhythms and harmonic structure and melodies of the song. Song listeners often face difficulty in finding desired tracks due to the vast volume of available music data. So, in this context, machine learning-based approaches can contribute in developing sophisticated method that can classify music genres and eventually building recommendation systems for online streaming services. In this paper, we propose an integrated framework that considers musical features from both time and frequency domain and after necessary preprocessing stages, incorporates into a boosting model for classification. We incorporate CatBoost as an ensemble learning model due to the obvious benefits of increased speed, reduced overfitting and the ability to assign greater weights to certain samples, and minimal variance sampling. We evaluated our proposed framework on a Bangla music dataset and discovered some noteworthy results that support the effectiveness of our proposed integrated model. A remarkable characteristic of such an integrated machine learning model, which is a significant contribution to the Bangla Music Industry in the era of Industrial Revolution 4.0, is its ability to analyze information from multidimensional data in a self-optimized approach with strong decision-making abilities.
Rizwan Hasan, Sohrab Hossain, Fahim Irfan Alam, Mannpreet Barua
