论文标题
将音乐知识纳入音乐生成的持续数据集扩展中
Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
论文作者
论文摘要
深度学习已迅速成为音乐发电的最新方法。但是，训练一个深层模型通常需要大型培训集，通常不适合特定的音乐风格。在本文中，我们介绍了增强生成（Aug-Gen），这是一种在资源约束领域训练的任何音乐生成系统的数据集扩展方法。该方法的关键直觉是，如果这些示例具有足够的高质量和多样性，则可以通过系统在培训过程中产生的示例来增强生成系统的培训数据。我们以J.S.巴赫（Bach），并表明这允许更长的培训并导致更好的生成产量。
Deep learning has rapidly become the state-of-the-art approach for music generation. However, training a deep model typically requires a large training set, which is often not available for specific musical styles. In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain. The key intuition of this method is that the training data for a generative system can be augmented by examples the system produces during the course of training, provided these examples are of sufficiently high quality and variety. We apply Aug-Gen to Transformer-based chorale generation in the style of J.S. Bach, and show that this allows for longer training and results in better generative output.
