论文标题
使用双向LSTM模型以及对MIDI数据训练的注意机制，以生成独特的音乐
Using a Bi-directional LSTM Model with Attention Mechanism trained on MIDI Data for Generating Unique Music
论文作者
论文摘要
在机器学习领域，生成音乐是一个有趣且充满挑战的问题。近年来，模仿人类的创造力一直很受欢迎，尤其是在计算机视觉和图像处理领域。随着甘斯的出现，可以根据训练有素的数据生成新的类似图像。但这对于音乐也不能这样做，因为音乐具有额外的时间维度。因此，有必要了解音乐是如何以数字形式代表的。当构建执行此生成任务的模型时，学习和生成部分将在某些高级表示（例如MIDI（乐器数字界面）或分数）中完成。本文提出了一个双向LSTM（长期记忆）模型，其注意机制能够基于MIDI数据生成相似类型的音乐。该模型产生的音乐遵循该模型的主题/风格。同样，由于MIDI的性质，可以定义和更改后的节奏，仪器和其他参数。
Generating music is an interesting and challenging problem in the field of machine learning. Mimicking human creativity has been popular in recent years, especially in the field of computer vision and image processing. With the advent of GANs, it is possible to generate new similar images, based on trained data. But this cannot be done for music similarly, as music has an extra temporal dimension. So it is necessary to understand how music is represented in digital form. When building models that perform this generative task, the learning and generation part is done in some high-level representation such as MIDI (Musical Instrument Digital Interface) or scores. This paper proposes a bi-directional LSTM (Long short-term memory) model with attention mechanism capable of generating similar type of music based on MIDI data. The music generated by the model follows the theme/style of the music the model is trained on. Also, due to the nature of MIDI, the tempo, instrument, and other parameters can be defined, and changed, post generation.
