"基于 RNN 的流行音乐生成模型研究"

Neural networks have revolutionized many fields, including the realm of music generation. In their paper titled "SONG FROM PI: A MUSICALLY PLAUSIBLE NETWORK FOR POP MUSIC GENERATION," Hang Chu, Raquel Urtasun, and Sanja Fidler from the Department of Computer Science at the University of Toronto in Canada, introduce a novel framework for creating pop music. Their model is a hierarchical Recurrent Neural Network that is designed to mimic the way pop music is typically composed. The layers and structure of the hierarchy are carefully crafted to reflect their prior knowledge of music composition. The bottom layers focus on generating the melody, while the higher layers incorporate more complex musical elements. The researchers' approach represents a significant advancement in the field of music generation, as it combines the power of neural networks with a deep understanding of music theory. By leveraging this hierarchical structure, their model is able to produce music that is not only musically plausible but also appealing to listeners. Overall, this research opens up new possibilities for the future of music composition and production. With the continued development of neural networks and other advanced technologies, we can expect to see even more innovative and exciting advancements in the field of music generation.