A novel Xi’an drum music generation method based on Bi-LSTM deep reinforcement learning

Chinese Folk Drum music is an excellent traditional cultural resource, it has brilliant historical and cultural heritage and excellent traditional cultural connotation. However, the survey found that the social and cultural values, tourism economic values, and national self-confidence embodied in folk drum music, such as Xi'an drum music, are far from being released, and even its own inheritance and development are facing difficulties. The research focuses on the automatic generation of Xi'an drum music, with the aim of further inheriting, developing, and utilizing this exceptional traditional cultural resource. While Artificial Intelligence (AI) music generation has gained popularity in recent years, most platforms primarily focus on modern music rather than Chinese folk music. To address these issues and the unique challenges faced by Xi'an drum music, this paper proposes a Bi-LSTM network-based deep reinforcement learning model. The model incorporates the distinctive characteristics of ancient Chinese music, such as pitch, chord, and mode, and utilizes the Actor-Critic algorithm in reinforcement learning. During the simulation generation stage, an improved method of generating strategies through reward and punishment scores is introduced. Additionally, the model takes into account abstract concept constraints, such as chord progression and music theory rules, which are translated into computer language. By constructing a chord reward mechanism and a music principle reward mechanism, the model achieves harmony constraints and enables the systematic generation of drum music. Experimental results demonstrate that the proposed model, based on Bi-LSTM deep reinforcement learning, can generate Xi'an drum music with high quality and artistic aesthetics. This research contributes to the preservation, development, and utilization of Xi'an drum music, leveraging advancements in AI music generation technology.