论文标题
基于深度学习变压器模式的游戏的自适应音乐生成体系结构
An adaptive music generation architecture for games based on the deep learning Transformer mode
论文作者
论文摘要
本文介绍了一种基于变压器深度学习模型为视频游戏生成音乐的体系结构。我们的动机是能够根据玩家的口味来定制这一代，他可以选择与他喜欢的音乐风格相对应的训练例子。该系统会生成各种音乐层，遵循作曲家设计视频游戏音乐的标准分层策略。为了使生成的音乐适应游戏和玩家的情况，我们正在使用一种唤醒的情感模型，以控制音乐层的选择。我们讨论了未来的当前局限性和前景，例如对音乐组件的协作和互动控制。
This paper presents an architecture for generating music for video games based on the Transformer deep learning model. Our motivation is to be able to customize the generation according to the taste of the player, who can select a corpus of training examples, corresponding to his preferred musical style. The system generates various musical layers, following the standard layering strategy currently used by composers designing video game music. To adapt the music generated to the game play and to the player(s) situation, we are using an arousal-valence model of emotions, in order to control the selection of musical layers. We discuss current limitations and prospects for the future, such as collaborative and interactive control of the musical components.
