论文标题
使用深度复发网络和嵌入的序列生成：音乐中的研究案例
Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music
论文作者
论文摘要
在过去的几年中，自动生成序列一直是一个高度探索的领域。特别是，由于机器学习和神经网络的最新进展和具有固有的记忆机制（例如复发性神经网络）的最新进展，自然语言处理和自动音乐作品已变得非常重要。本文评估了不同类型的记忆机制（存储单元），并分析其在音乐组成领域的性能。所提出的方法考虑了音乐理论概念，例如换位，并使用数据转换（嵌入）引入语义含义并提高生成的旋律的质量。提出了一组定量指标，以自动评估所提出的体系结构的性能，从而测量音乐作品的音调。
Automatic generation of sequences has been a highly explored field in the last years. In particular, natural language processing and automatic music composition have gained importance due to the recent advances in machine learning and Neural Networks with intrinsic memory mechanisms such as Recurrent Neural Networks. This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. The proposed approach considers music theory concepts such as transposition, and uses data transformations (embeddings) to introduce semantic meaning and improve the quality of the generated melodies. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically, measuring the tonality of the musical compositions.
