Safeguarding Music Artists Through Machine Learning Based Singer Classification

Across various fields, the area of Bengali music, with its rich cultural heritage, is highly unexplored but identifying and classifying singers has become vital for safeguarding intellectual property and encouraging the music industry. Precise identification of Bengali singers is crucial for preserving singers’ rights and ensuring fair compensation. This paper shows the effectiveness of different Convolutional Recurrent Neural Network (CRNN) and Recurrent Neural Network (RNN) based architectures on singer classification, specifically in Bengali songs, leveraging Mel-spectrogram features. In our study, the superiority of CRNN models over RNN and traditional ML models has been showcased, and among ML models our ensemble technique outperformed the rest. Our CRNN model achieved an F1-score of 0.97, significantly outperforming traditional ML methods (F1-scores for Logistic Regression, Random Forest, SVM, KNN, and Ensemble are 0.76, 0.74, 0.72, 0.68 and 0.83 respectively). The significance of CRNN in singer classification for Bengali music has been highlighted in our study, which offers a solution to protect singers’ rights by reducing copyright infringement. Additionally, our work has the potential to assist in the development of music recommendation systems.