MCRN: A New Content-Based Music Classification and Recommendation Network

Music classification and recommendation have received wide-spread attention in recent years. However, content-based deep music classification approaches are still very rare. Meanwhile, existing music recommendation systems generally rely on collaborative filtering. Unfortunately, this method has serious cold start problem. In this paper, we propose a simple yet effective convolutional neural network named MCRN (short for music classification and recommendation network), for learning the audio content features of music, and facilitating music classification and recommendation. Concretely, to extract the content features of music, the audio is converted into “spectrograms” by Fourier transform. MCRN can effectively extract music content features from the spectrograms. Experimental results show that MCRN outperforms other compared models on music classification and recommendation tasks, demonstrating its superiority over previous approaches.

Duke Scholars

Author Kaizhu Huang DKU Faculty