论文标题
Deepdrummer：使用深度学习产生鼓循环，在循环中产生人类
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
论文作者
论文摘要
DeepDrummer是一种鼓循环生成工具，它使用主动学习来从少数交互中学习人类用户的偏好（或当前的艺术意图）。该工具的主要目标是有效探索新的音乐思想。我们在音频数据上训练深层神经网络分类器，并显示如何将其用作系统的核心组件，该系统基于几个以前关于如何构建这些循环的信念生成鼓循环。 我们旨在建立一个可以收敛到有意义结果的系统，即使与用户的交互数量有限。此属性使我们的方法可以从冷启动情况（没有预先存在的数据集）或用户提供的音频样本集合开始。在与25名参与者的概念研究证明中，我们从经验上证明，Deepdrummer能够在少量相互作用后融合我们受试者的偏好。
DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions. The principal goal of this tool is to enable an efficient exploration of new musical ideas. We train a deep neural network classifier on audio data and show how it can be used as the core component of a system that generates drum loops based on few prior beliefs as to how these loops should be structured. We aim to build a system that can converge to meaningful results even with a limited number of interactions with the user. This property enables our method to be used from a cold start situation (no pre-existing dataset), or starting from a collection of audio samples provided by the user. In a proof of concept study with 25 participants, we empirically demonstrate that DeepDrummer is able to converge towards the preference of our subjects after a small number of interactions.
