人工神经网络对拍干扰
Artificial Neural Networks Jamming on the Beat
摘要：
本文讨论的问题是长期的相关性，是典型的符号音乐，是现代生成算法的挑战。它为这个挑战提出了一个非常简单的解决方案，即生成一个鼓模式，可以进一步用作旋律生成的基础。本文提出了一个大型数据集的鼓模式与相应的旋律。它探讨了两种可能的方法鼓模式生成。探索鼓型的潜在空间，可以生成具有特定音乐风格的新鼓型。最后，本文证明了一个简单的人工神经网络可以被训练来生成与这些鼓模式作为输入相对应的旋律。所得到的系统可以用于具有歌曲式结构和音符之间的更高的长尺度相关性的符号音乐的端到端生成。
英文摘要：
This paper addresses the issue of long-scale correlations that is characteristic for symbolic music and is a challenge for modern generative algorithms. It suggests a very simple workaround for this challenge, namely, generation of a drum pattern that could be further used as a foundation for melody generation. The paper presents a large dataset of drum patterns alongside with corresponding melodies. It explores two possible methods for drum pattern generation. Exploring a latent space of drum patterns one could generate new drum patterns with a given music style. Finally, the paper demonstrates that a simple artificial neural network could be trained to generate melodies corresponding with these drum patters used as inputs. Resulting system could be used for end-to-end generation of symbolic music with song-like structure and higher long-scale correlations between the notes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
