MemoMusic 3.0: Considering Context at Music Recommendation and Combining Music Theory at Music Generation
Luntian Mou, Yihan Sun, Yunhan Tian, Yiqi Sun, Yuhang Liu, Zexi Zhang, Ruichen He, Juehui Li, Jueying Li, Zijin Li, Feng Gao, Yemin Shi, Ramesh Jain · 2023
MemoMusic 3.0 enhances personalized music recommendation by considering the music listening context, and improves music generation by introducing music theory. One observation is that the context of music listening would affect the emotional states of listeners, positively or negatively. The other is that better music can be generated by introducing some music theory knowledge. We propose a Transformer-based music generation framework, which is trained into three models for Classic, Pop, and Yanni music respectively. The dominant melody of a music with expected Valence and Arousal values is used as a sample sequence to the model, and its output is adjusted according to music theory. Experimental results demonstrate that MemoMusic 3.0 performs better at improving the emotional states of listeners and achieves better user satisfaction.