Neural networks and deep learning are often deployed for the sake of the most comprehensive music generation with as little involvement as possible from the human musician. Implementations in aid of, or being a tool for, music practitioners are sparse. This paper proposes the integration of generative stacked autoencoder structures for rhythm generation, within a conventional melodic step-sequencer. It further aims to work towards its implementation being accessible to the average electronic music practitioner. Several model architectures have been trained and tested for their creative potential. While the currently implementations do display limitations, they do represent viable creative solutions for music practitioners.

翻译：神经网络与深度学习通常被用于尽可能少地依赖人类音乐家，以实现最全面的音乐生成。旨在辅助音乐从业者或作为其工具的实现方案较为罕见。本文提出将生成式堆叠自编码器结构集成到传统的旋律步进音序器中，以用于节奏生成。本文进一步旨在推动其实现方式能够被普通电子音乐从业者所使用。我们训练并测试了多种模型架构，以评估其创作潜力。尽管当前的实现方案存在局限性，但它们仍为音乐从业者提供了可行的创意解决方案。