论文标题
随时间结构增强音乐的音乐发电
Music Generation with Temporal Structure Augmentation
论文作者
论文摘要
在本文中，我们介绍了一种新型功能增强方法，用于生成包括旋律和和声的结构化音乐作品。所提出的方法增加了一个连接的生成模型，该模型降低了歌曲结论和仪表标记，这是研究神经网络是否可以学会产生更令人愉悦和结构化的音乐输出的额外输入功能，这是由于增强了具有结构性特征的输入数据。使用LSTM细胞的RNN体系结构以有监督的序列学习设置在诺丁汉民间音乐数据集上进行了培训，遵循音乐语言建模方法，然后应用于和谐和旋律的产生。我们的实验显示了两种注释的预测性能的改进。还使用在线图灵风格的听力测试对生成的音乐进行了主观评估，该测试证实了美学质量和使用时间结构产生的音乐的感知结构的实质性改善。
In this paper we introduce a novel feature augmentation approach for generating structured musical compositions comprising melodies and harmonies. The proposed method augments a connectionist generation model with count-down to song conclusion and meter markers as extra input features to study whether neural networks can learn to produce more aesthetically pleasing and structured musical output as a consequence of augmenting the input data with structural features. An RNN architecture with LSTM cells is trained on the Nottingham folk music dataset in a supervised sequence learning setup, following a Music Language Modelling approach, and then applied to generation of harmonies and melodies. Our experiments show an improved prediction performance for both types of annotation. The generated music was also subjectively evaluated using an on-line Turing style listening test which confirms a substantial improvement in the aesthetic quality and in the perceived structure of the music generated using the temporal structure.
