DeepDrummer：使用深度学习和人在循环中生成鼓循环
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
摘要：
DeepDrummer是一个鼓循环生成工具，它使用主动学习从少量的交互中学习人类用户的偏好（或当前的艺术意图）。这个工具的主要目标是使新的音乐思想的有效探索。我们训练音频数据的深度神经网络分类器，并展示了它如何被用作系统的核心组件，该系统基于很少的先验信念来生成鼓循环，这些循环应该如何构造。我们的目标是建立一个系统，即使与用户的交互次数有限，也可以收敛到有意义的结果。这个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）开始使用，或者从用户提供的音频样本集合开始使用。在一项有25名参与者的概念验证研究中，我们凭经验证明了DeepDrummer能够在少量交互后收敛到我们受试者的偏好。
英文摘要：
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.
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。
